subject:(genetic programming)

Victoria University of Wellington

1. Downey, Carlton. Explorations in Parallel Linear Genetic Programming.

Degree: 2011, Victoria University of Wellington

► Linear Genetic Programming (LGP) is a powerful problem-solving technique, but one with several significant weaknesses. LGP programs consist of a linear sequence of instructions, where… (more)

Subjects/Keywords: Genetic programming

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APA (6^th Edition):

Downey, C. (2011). Explorations in Parallel Linear Genetic Programming. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/2312

Chicago Manual of Style (16^th Edition):

Downey, Carlton. “Explorations in Parallel Linear Genetic Programming.” 2011. Masters Thesis, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/2312.

MLA Handbook (7^th Edition):

Downey, Carlton. “Explorations in Parallel Linear Genetic Programming.” 2011. Web. 14 Apr 2021.

Vancouver:

Downey C. Explorations in Parallel Linear Genetic Programming. [Internet] [Masters thesis]. Victoria University of Wellington; 2011. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/2312.

Council of Science Editors:

Downey C. Explorations in Parallel Linear Genetic Programming. [Masters Thesis]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/2312

University of Hawaii – Manoa

2. Nakatsu, Jill Sachie Kobashigawa. Genetic programming applications in electromagnetics.

Degree: 2016, University of Hawaii – Manoa

URL: http://hdl.handle.net/10125/101315

►

M.S. University of Hawaii at Manoa 2012.

This thesis considers the application of Genetic Programming (GP) to create computer programs that can solve both classification… (more)

▼

M.S. University of Hawaii at Manoa 2012.

This thesis considers the application of Genetic Programming (GP) to create computer programs that can solve both classification and metamaterial design problems in the area of electromagnetics (EM). Specifically, GP is used to develop an automatic target classification algorithm and is combined with the patterning Lindenmayer system for the development of a metamaterial design program. For both studies, GP is compared to other popular artificial intelligence (AI) techniques in each area such as the Neural Networks (NN) and the Genetic Algorithm (GA) methods. It is shown that Genetic Programming provides improved classification results and when applied to design work leads to unconventional and global optimal solutions. In the target classification of buried objects it is desired to develop an accurate and reliable analysis and classification of electromagnetic data for buried unexploded ordnance (UXO) discrimination. The classification of this data is vital to not only clear buried UXO leftover from war and military training areas across the world with minimal false alarm rates but also to provide opportunities to use this land for housing and business development. GP is compared with neural networks, a popular classification technique, for the classification of UXO scattering patterns. Three classification scenarios with various levels of difficulty were examined and in all cases GP outperformed the NNs. For the metamaterial design study, a GP program was developed that generates novel, efficient, and unintuitive "broadband" metamaterial designs. There has been no established methodology for developing a successful design of ultra wideband and low frequency metamaterial structures and to this end; GP is used to investigate the development of unconventional designs. A metamaterial design system combining GP with Lindenmayer system (L-system) patterning rules was developed and utilized in the study. A Matlab toolbox which controls both the GP algorithm and the full EM wave simulation in HFSS was also developed and utilized in the comparison of the GP-L system to the genetic algorithm. It is shown that GP is indeed capable of developing designs with improved performance from those reported using the GA methods. This thesis includes a detailed description of the developed GP code, fitness function, and obtained results from both studies.

Subjects/Keywords: genetic programming; electromagnetics

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APA (6^th Edition):

Nakatsu, J. S. K. (2016). Genetic programming applications in electromagnetics. (Thesis). University of Hawaii – Manoa. Retrieved from http://hdl.handle.net/10125/101315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nakatsu, Jill Sachie Kobashigawa. “Genetic programming applications in electromagnetics.” 2016. Thesis, University of Hawaii – Manoa. Accessed April 14, 2021. http://hdl.handle.net/10125/101315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nakatsu, Jill Sachie Kobashigawa. “Genetic programming applications in electromagnetics.” 2016. Web. 14 Apr 2021.

Vancouver:

Nakatsu JSK. Genetic programming applications in electromagnetics. [Internet] [Thesis]. University of Hawaii – Manoa; 2016. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10125/101315.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nakatsu JSK. Genetic programming applications in electromagnetics. [Thesis]. University of Hawaii – Manoa; 2016. Available from: http://hdl.handle.net/10125/101315

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade Nova

3. Scott, Kristen Marie. A multiple expression alignment framework for genetic programming.

Degree: 2018, Universidade Nova

URL: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/40749

► Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned… (more)

Subjects/Keywords: Genetic Programming; Geometric Semantic Genetic Programming; Error Space Genetic Programming

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APA (6^th Edition):

Scott, K. M. (2018). A multiple expression alignment framework for genetic programming. (Thesis). Universidade Nova. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/40749

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Scott, Kristen Marie. “A multiple expression alignment framework for genetic programming.” 2018. Thesis, Universidade Nova. Accessed April 14, 2021. https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/40749.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Scott, Kristen Marie. “A multiple expression alignment framework for genetic programming.” 2018. Web. 14 Apr 2021.

Vancouver:

Scott KM. A multiple expression alignment framework for genetic programming. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2021 Apr 14]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/40749.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Scott KM. A multiple expression alignment framework for genetic programming. [Thesis]. Universidade Nova; 2018. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/40749

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of KwaZulu-Natal

4. Ragalo, Anisa Waganda. Evolving dynamic fitness measures for genetic programming.

Degree: 2018, University of KwaZulu-Natal

URL: https://researchspace.ukzn.ac.za/handle/10413/18092

► This research proposes dynamic fitness measure genetic programming (DFMGP). DFMGP modifies the conventional genetic programming (GP) approach: rather than applying a single fitness measure individually… (more)

▼ This research proposes dynamic fitness measure genetic programming (DFMGP). DFMGP modifies the conventional genetic programming (GP) approach: rather than applying a single fitness measure individually throughout GP, a different fitness measure (or combination of fitness measures) is applied on each GP generation. A detailed review of the fitness measures used in GP is presented. The review demonstrates that different fitness measures were introduced to overcome different shortcomings, e.g. escaping local optima, reducing bloat, thereby improving on the performance of the GP algorithm. A subsequent analysis of the fitness measures shows that there is no universal “best” fitness measure; rather, different fitness measures are appropriate for different problems. The literature also anticipates that applying different fitness measures at different points of the GP problem solving process should be more effective then applying a single fitness measure throughout the algorithm. Hence the case for DFMGP. Selecting the fitness measures to apply on each GP generation is in itself a combinatorial optimization problem: the study investigates two approaches to serve this purpose, namely, a genetic algorithm and genetic programming. The genetic algorithm (GA) derives a sequence of fitness measures to be applied, while GP produces an arithmetic function combining the fitness measures. The performance of DFMGP applying the evolved fitness measure sequences and DFMGP applying the evolved fitness measure combinations is compared to the conventional GP approach on a number of benchmark and complex, real-world problems. DFMGP is found to be more effective than standard GP. The study also reveals that both the sequences and arithmetic combinations of the fitness measures are effective when applied to problem instances different from those used to derive them. Hence, the sequences and arithmetic combinations are reusable, whereby simpler problems are used for derivation, and DFMGP applying the derived fitness measures is then used to solve more complex problems. Therefore the time necessary for the derivations is reduced. An analysis of the evolved sequences and arithmetic combinations of the fitness measures shows that fitness measures applied in the preliminary DFMGP generations support exploration while those applied in later DFMGP generations support exploitation. GP search is a constant balance between exploration and exploitation, with the former being more suited to the preliminary generations, and the latter, later generations. DFMGP’s performance advantage over standard GP is therefore justified by the premise that the fitness measure used on each generation combinations derived by GP is also found to perform better than DFMGP applying the fitness measure sequences derived by the GA. The former approach facilitates combining explorative and exploitative fitness measures on some of the DFMGP generations, whereby rather than simply switching between exploration and exploitation, the fitness measure… Advisors/Committee Members: Pillay, Nelishia. (advisor).

Subjects/Keywords: Genetic programming.; Programming.; Dynamic fitness measure genetic programming.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ragalo, A. W. (2018). Evolving dynamic fitness measures for genetic programming. (Thesis). University of KwaZulu-Natal. Retrieved from https://researchspace.ukzn.ac.za/handle/10413/18092

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Ragalo, Anisa Waganda. “Evolving dynamic fitness measures for genetic programming.” 2018. Thesis, University of KwaZulu-Natal. Accessed April 14, 2021. https://researchspace.ukzn.ac.za/handle/10413/18092.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Ragalo, Anisa Waganda. “Evolving dynamic fitness measures for genetic programming.” 2018. Web. 14 Apr 2021.

Vancouver:

Ragalo AW. Evolving dynamic fitness measures for genetic programming. [Internet] [Thesis]. University of KwaZulu-Natal; 2018. [cited 2021 Apr 14]. Available from: https://researchspace.ukzn.ac.za/handle/10413/18092.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ragalo AW. Evolving dynamic fitness measures for genetic programming. [Thesis]. University of KwaZulu-Natal; 2018. Available from: https://researchspace.ukzn.ac.za/handle/10413/18092

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Victoria University of Wellington

5. Liang, Yuyu. Genetic Programming for Supervised Figure-ground Image Segmentation.

Degree: 2018, Victoria University of Wellington

URL: http://hdl.handle.net/10063/6923

► Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is essential to various applications in computer vi- sion and image… (more)

▼ Figure-ground segmentation is a process of separating regions of interest from unimportant backgrounds. It is essential to various applications in computer vi- sion and image processing, e.g. object tracking and image editing, as they are only interested in certain regions of an image and use figure-ground segmenta- tion as a pre-processing step. Traditional figure-ground segmentation methods often require heavy human workload (e.g. ground truth labeling), and/or rely heavily on human guidance (e.g. locating an initial model), accordingly cannot easily adapt to diverse image domains. Evolutionary computation (EC) is a family of algorithms for global optimi- sation, which are inspired by biological evolution. As an EC technique, genetic programming (GP) can evolve algorithms automatically for complex problems without pre-defining solution models. Compared with other EC techniques, GP is more flexible as it can utilise complex and variable-length representations (e.g. trees) of candidate solutions. It is hypothesised that this flexibility of GP makes it possible to evolve better solutions than those designed by experts. However, there have been limited attempts at applying GP to figure-ground segmentation. In this thesis, GP is enabled to successfully address figure-ground segmentation through evolving well-performing segmentors and generating effective features. The objectives are to investigate various image features as inputs of GP, develop multi-objective approaches, develop feature selection/construction methods, and conduct further evaluations of the proposed GP methods. The following new methods have been developed. Effective terminal sets of GP are investigated for figure-ground segmentation, covering three general types of image features, i.e. colour/brightness, texture and shape features. Results show that texture features are more effective than intensities and shape features as they are discriminative for different materials that foreground and background regions normally belong to (e.g. metal or wood). Two new multi-objective GP methods are proposed to evolve figure-ground segmentors, aiming at producing solutions balanced between the segmentation performance and solution complexity. Compared with a reference method that does not consider complexity and a parsimony pressure based method (a popular bloat control technique), the proposed methods can significantly reduce the solution size while achieving similar segmentation performance based on the Mann- Whitney U-Test at the significance level 5%. GP is introduced for the first time to conduct feature selection for figure- ground segmentation tasks, aiming to maximise the segmentation performance and minimise the number of selected features. The proposed methods produce feature subsets that lead to solutions achieving better segmentation performance with lower features than those of two benchmark methods (i.e. sequential forward selection and sequential backward selection) and the original full feature set. This is due to GP’s high search ability and… Advisors/Committee Members: Zhang, Mengjie, Browne, Will N..

Subjects/Keywords: Genetic programming; Segmentation; Feature

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liang, Y. (2018). Genetic Programming for Supervised Figure-ground Image Segmentation. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/6923

Chicago Manual of Style (16^th Edition):

Liang, Yuyu. “Genetic Programming for Supervised Figure-ground Image Segmentation.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/6923.

MLA Handbook (7^th Edition):

Liang, Yuyu. “Genetic Programming for Supervised Figure-ground Image Segmentation.” 2018. Web. 14 Apr 2021.

Vancouver:

Liang Y. Genetic Programming for Supervised Figure-ground Image Segmentation. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/6923.

Council of Science Editors:

Liang Y. Genetic Programming for Supervised Figure-ground Image Segmentation. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/6923

Victoria University of Wellington

6. Chen, Qi. Improving the Generalisation of Genetic Programming for Symbolic Regression.

Degree: 2018, Victoria University of Wellington

URL: http://hdl.handle.net/10063/7029

► Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables… (more)

▼ Symbolic regression (SR) is a function identification process, the task of which is to identify and express the relationship between the input and output variables in mathematical models. SR is named to emphasise its ability to find the structure and coefficients of the model simultaneously. Genetic Programming (GP) is an attractive and powerful technique for SR, since it does not require any predefined model and has a flexible representation. However, GP based SR generally has a poor generalisation ability which degrades its reliability and hampers its applications to science and real-world modeling. Therefore, this thesis aims to develop new GP approaches to SR that evolve/learn models exhibiting good generalisation ability. This thesis develops a novel feature selection method in GP for high-dimensional SR. Feature selection can potentially contribute not only to improving the efficiency of learning algorithms but also to enhancing the generalisation ability. However, feature selection is seldom considered in GP for high-dimensional SR. The proposed new feature selection method utilises GP’s built-in feature selection ability and relies on permutation to detect the truly relevant features and discard irrelevant/noisy features. The results confirm the superiority of the proposed method over the other examined feature selection methods including random forests and decision trees on identifying the truly relevant features. Further analysis indicates that the models evolved by GP with the proposed feature selection method are more likely to contain only the truly relevant features and have better interpretability. To address the overfitting issue of GP when learning from a relatively small number of instances, this thesis proposes a new GP approach by incorporating structural risk minimisation (SRM), which is a framework to estimate the generalisation performance of models, into GP. The effectiveness of SRM highly depends on the accuracy of the Vapnik-Chervonenkis (VC) dimension measuring model complexity. This thesis significantly extends an experimental method (instead of theoretical estimation) to measure the VC-dimension of a mixture of linear and nonlinear regression models in GP for the first time. The experimental method has been conducted using uniform and non-uniform settings and provides reliable VC-dimension values. The results show that our methods have an impressively better generalisation gain and evolve more compact model, which have a much smaller behavioural difference from the target models than standard GP and GP with bootstrap, The proposed method using the optimised non-uniform setting further improves the one using the uniform setting. This thesis employs geometric semantic GP (GSGP) to tackle the unsatisfied generalisation performance of GP for SR when no overfitting occurs. It proposes three new angle-awareness driven geometric semantic operators (GSO) including selection, crossover and mutation to further explore the geometry of the semantic space to gain a greater generalisation… Advisors/Committee Members: Zhang, Mengjie, Xue, Bing.

Subjects/Keywords: Genetic Programming; Symbolic regression; Generalisation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Chen, Q. (2018). Improving the Generalisation of Genetic Programming for Symbolic Regression. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/7029

Chicago Manual of Style (16^th Edition):

Chen, Qi. “Improving the Generalisation of Genetic Programming for Symbolic Regression.” 2018. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/7029.

MLA Handbook (7^th Edition):

Chen, Qi. “Improving the Generalisation of Genetic Programming for Symbolic Regression.” 2018. Web. 14 Apr 2021.

Vancouver:

Chen Q. Improving the Generalisation of Genetic Programming for Symbolic Regression. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2018. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/7029.

Council of Science Editors:

Chen Q. Improving the Generalisation of Genetic Programming for Symbolic Regression. [Doctoral Dissertation]. Victoria University of Wellington; 2018. Available from: http://hdl.handle.net/10063/7029

Victoria University of Wellington

7. Smart, Will. Empirical Analysis of Schemata in Genetic Programming.

Degree: 2011, Victoria University of Wellington

URL: http://hdl.handle.net/10063/1826

► Schemata and buiding blocks have been used in Genetic Programming (GP) in several contexts including subroutines, theoretical analysis and for empirical analysis. Of these three… (more)

▼ Schemata and buiding blocks have been used in Genetic Programming (GP) in several contexts including subroutines, theoretical analysis and for empirical analysis. Of these three the least explored is empirical analysis. This thesis presents a powerful GP empirical analysis technique for analysis of all schemata of a given form occurring in any program of a given population at scales not previously possible for the kinds of global analysis performed. There are many competing GP forms of schema and, rather than choosing one for analysis, the thesis defines the match-tree meta-form of schema as a general language expressing forms of schema for use by the analysis system. This language can express most forms of schema previously used in tree-based GP. The new method can perform wide-ranging analyses on the prohibitively large set of all schemata in the programs by introducing the concepts of maximal schema, maximal program subset, representative set of schemata, and representative program subset. These structures are used to optimize the analysis, shrinking its complexity to a manageable size without sacrificing the result. Characterization experiments analyze GP populations of up to 501 60- node programs, using 11 forms of schema including rooted-hyperschemata and non-rooted fragments. The new method has close to quadratic complexity on population size, and quartic complexity on program size. Efficacy experiments present example analyses using the new method. The experiments offer interesting insights into the dynamics of GP runs including fine-grained analysis of convergence and the visualization of schemata during a GP evolution. Future work will apply the many possible extensions of this new method to understanding how GP operates, including studies of convergence, building blocks and schema fitness. This method provides a much finer-resolution microscope into the inner workings of GP and will be used to provide accessable visualizations of the evolutionary process. Advisors/Committee Members: Zhang, Mengjie, Andreae, Peter.

Subjects/Keywords: Empirical analysis; Schemata; Genetic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Smart, W. (2011). Empirical Analysis of Schemata in Genetic Programming. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/1826

Chicago Manual of Style (16^th Edition):

Smart, Will. “Empirical Analysis of Schemata in Genetic Programming.” 2011. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/1826.

MLA Handbook (7^th Edition):

Smart, Will. “Empirical Analysis of Schemata in Genetic Programming.” 2011. Web. 14 Apr 2021.

Vancouver:

Smart W. Empirical Analysis of Schemata in Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2011. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/1826.

Council of Science Editors:

Smart W. Empirical Analysis of Schemata in Genetic Programming. [Doctoral Dissertation]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/1826

Victoria University of Wellington

8. Nguyen, Su. Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming.

Degree: 2013, Victoria University of Wellington

URL: http://hdl.handle.net/10063/3018

► Scheduling is an important planning activity in manufacturing systems to help optimise the usage of scarce resources and improve the customer satisfaction. In the job… (more)

▼ Scheduling is an important planning activity in manufacturing systems to help optimise the usage of scarce resources and improve the customer satisfaction. In the job shop manufacturing environment, scheduling problems are challenging due to the complexity of production flows and practical requirements such as dynamic changes, uncertainty, multiple objectives, and multiple scheduling decisions. Also, job shop scheduling (JSS) is very common in small manufacturing businesses and JSS is considered one of the most popular research topics in this domain due to its potential to dramatically decrease the costs and increase the throughput. Practitioners and researchers have applied different computational techniques, from different fields such as operations research and computer science, to deal with JSS problems. Although optimisation methods usually show their dominance in the literature, applying optimisation techniques in practical situations is not straightforward because of the practical constraints and conditions in the shop. Dispatching rules are a very useful approach to dealing with these environments because they are easy to implement(by computers and shop floor operators) and can cope with dynamic changes. However, designing an effective dispatching rule is not a trivial task and requires extensive knowledge about the scheduling problem. The overall goal of this thesis is to develop a genetic programming based hyper-heuristic (GPHH) approach for automatic heuristic design of reusable and competitive dispatching rules in job shop scheduling environments. This thesis focuses on incorporating special features of JSS in the representations and evolutionary search mechanisms of genetic programming(GP) to help enhance the quality of dispatching rules obtained. This thesis shows that representations and evaluation schemes are the important factors that significantly influence the performance of GP for evolving dispatching rules. The thesis demonstrates that evolved rules which are trained to adapt their decisions based on the changes in shops are better than conventional rules. Moreover, by applying a new evaluation scheme, the evolved rules can effectively learn from the mistakes made in previous completed schedules to construct better scheduling decisions. The GP method using the newproposed evaluation scheme shows better performance than the GP method using the conventional scheme. This thesis proposes a new multi-objective GPHH to evolve a Pareto front of non-dominated dispatching rules. Instead of evolving a single rule with assumed preferences over different objectives, the advantage of this GPHH method is to allow GP to evolve rules to handle multiple conflicting objectives simultaneously. The Pareto fronts obtained by the GPHH method can be used as an effective tool to help decision makers select appropriate rules based on their knowledge regarding possible trade-offs. The thesis shows that evolved rules can dominate well-known dispatching rules when a single objective and multiple objectives are… Advisors/Committee Members: Zhang, Mengjie, Johnston, Mark.

Subjects/Keywords: Genetic programming; Scheduling; Heuristic

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Nguyen, S. (2013). Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/3018

Chicago Manual of Style (16^th Edition):

Nguyen, Su. “Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming.” 2013. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/3018.

MLA Handbook (7^th Edition):

Nguyen, Su. “Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming.” 2013. Web. 14 Apr 2021.

Vancouver:

Nguyen S. Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2013. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/3018.

Council of Science Editors:

Nguyen S. Automatic Design of Dispatching Rules for Job Shop Scheduling with Genetic Programming. [Doctoral Dissertation]. Victoria University of Wellington; 2013. Available from: http://hdl.handle.net/10063/3018

Victoria University of Wellington

9. Hunt, Rachel. Genetic Programming Hyper-heuristics for Job Shop Scheduling.

Degree: 2016, Victoria University of Wellington

URL: http://hdl.handle.net/10063/5219

► Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere.… (more)

▼ Scheduling problems arise whenever there is a choice of order in which a number of tasks should be performed; they arise commonly, daily and everywhere. A job shop is a common manufacturing environment in which a schedule for processing a set of jobs through a set of machines needs to be constructed. Job shop scheduling (JSS) has been called a fascinating challenge as it is computationally hard and prevalent in the real-world. Developing more effective ways of scheduling jobs could increase profitability through increasing throughput and decreasing costs. Dispatching rules (DRs) are one of the most popular scheduling heuristics. DRs are easy to implement, have low computational cost, and cope well with the dynamic nature of real-world manufacturing environments. However, the manual development of DRs is time consuming and requires expert knowledge of the scheduling environment. Genetic programming (GP) is an evolutionary computation method which is ideal for automatically discovering DRs. This is a hyper-heuristic approach, as GP is searching the search space of heuristic (DR) solutions rather than constructing a schedule directly. The overall goal of this thesis is to develop GP based hyper-heuristics for the efficient evolution (automatic generation) of robust, reusable and effective scheduling heuristics for JSS environments, with greater interpretability. Firstly, this thesis investigates using GP to evolve optimal DRs for the static two-machine JSS problem with makespan objective function. The results show that some evolved DRs were equivalent to an optimal scheduling algorithm. This validates both the GP based hyper-heuristic approach for generating DRs for JSS and the representation used. Secondly, this thesis investigates developing ``less-myopic'' DRs through the use of wider-looking terminals and local search to provide additional fitness information. The results show that incorporating features of the state of the wider shop improves the mean performance of the best evolved DRs, and that the inclusion of local search in evaluation evolves DRs which make better decisions over the local time horizon, and attain lower total weighted tardiness. Thirdly, this thesis proposes using strongly typed GP (STGP) to address the challenging issue of interpretability of DRs evolved by GP. Several grammars are investigated and the results show that the DRs evolved in the semantically constrained search space of STGP do not have (on average) performance that is as good as unconstrained. However, the interpretability of evolved rules is substantially improved. Fourthly, this thesis investigates using multiobjective GP to encourage evolution of DRs which are more readily interpretable by human operators. This approach evolves DRs with similar performance but smaller size. Fragment analysis identifies popular combinations of terminals which are then used as high level terminals; the inclusion of these terminals improved the mean performance of the best evolved DRs. Through this thesis the following major… Advisors/Committee Members: Zhang, Mengjie, Johnston, Mark.

Subjects/Keywords: Genetic Programming; Hyper-heuristics; Scheduling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hunt, R. (2016). Genetic Programming Hyper-heuristics for Job Shop Scheduling. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/5219

Chicago Manual of Style (16^th Edition):

Hunt, Rachel. “Genetic Programming Hyper-heuristics for Job Shop Scheduling.” 2016. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/5219.

MLA Handbook (7^th Edition):

Hunt, Rachel. “Genetic Programming Hyper-heuristics for Job Shop Scheduling.” 2016. Web. 14 Apr 2021.

Vancouver:

Hunt R. Genetic Programming Hyper-heuristics for Job Shop Scheduling. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2016. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/5219.

Council of Science Editors:

Hunt R. Genetic Programming Hyper-heuristics for Job Shop Scheduling. [Doctoral Dissertation]. Victoria University of Wellington; 2016. Available from: http://hdl.handle.net/10063/5219

10. Frade, Miguel. Genetic terrain programming.

Degree: 2008, Instituto Politécnico de Leiria

URL: http://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/72

►

Dissertação apresentada à Universidad de Extremadura para obtenção do Diploma de Estudios Avanzados, orientada por Francisco Fernandéz de Vega e Carlos Cotta.

Nowadays there are… (more)

Subjects/Keywords: Genetic terrain programming; Terrain generator

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APA (6^th Edition):

Frade, M. (2008). Genetic terrain programming. (Thesis). Instituto Politécnico de Leiria. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/72

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Frade, Miguel. “Genetic terrain programming.” 2008. Thesis, Instituto Politécnico de Leiria. Accessed April 14, 2021. http://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/72.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Frade, Miguel. “Genetic terrain programming.” 2008. Web. 14 Apr 2021.

Vancouver:

Frade M. Genetic terrain programming. [Internet] [Thesis]. Instituto Politécnico de Leiria; 2008. [cited 2021 Apr 14]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/72.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Frade M. Genetic terrain programming. [Thesis]. Instituto Politécnico de Leiria; 2008. Available from: http://www.rcaap.pt/detail.jsp?id=oai:iconline.ipleiria.pt:10400.8/72

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Limerick

11. Nicolau, Miguel. Genetic Algorithms using Grammatical Evolution.

Degree: 2017, University of Limerick

URL: http://hdl.handle.net/10197/8262

►

This thesis proposes a new representation for genetic algorithms, based on the idea of a genotype to phenotype mapping process. It allows the explicit encoding… (more)

Subjects/Keywords: Genetic programming; Grammatical evolution

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APA (6^th Edition):

Nicolau, M. (2017). Genetic Algorithms using Grammatical Evolution. (Thesis). University of Limerick. Retrieved from http://hdl.handle.net/10197/8262

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Nicolau, Miguel. “Genetic Algorithms using Grammatical Evolution.” 2017. Thesis, University of Limerick. Accessed April 14, 2021. http://hdl.handle.net/10197/8262.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Nicolau, Miguel. “Genetic Algorithms using Grammatical Evolution.” 2017. Web. 14 Apr 2021.

Vancouver:

Nicolau M. Genetic Algorithms using Grammatical Evolution. [Internet] [Thesis]. University of Limerick; 2017. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10197/8262.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nicolau M. Genetic Algorithms using Grammatical Evolution. [Thesis]. University of Limerick; 2017. Available from: http://hdl.handle.net/10197/8262

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology

12. van Ramshorst, Justus (author). Genetic Programming in Hydrology: Using genetic programming in conceptual modelling.

Degree: 2017, Delft University of Technology

URL: http://resolver.tudelft.nl/uuid:e1c648c7-a28f-4a9f-be20-22cc6c2c80eb

►

This report introduces the use of Genetic Programming (GP) into hydrology by describing the results of GP using conceptual hydrological models as physical representation. First… (more)

Subjects/Keywords: Genetic programming; Conceptual modelling; Hydrology

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APA (6^th Edition):

van Ramshorst, J. (. (2017). Genetic Programming in Hydrology: Using genetic programming in conceptual modelling. (Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e1c648c7-a28f-4a9f-be20-22cc6c2c80eb

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

van Ramshorst, Justus (author). “Genetic Programming in Hydrology: Using genetic programming in conceptual modelling.” 2017. Thesis, Delft University of Technology. Accessed April 14, 2021. http://resolver.tudelft.nl/uuid:e1c648c7-a28f-4a9f-be20-22cc6c2c80eb.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

van Ramshorst, Justus (author). “Genetic Programming in Hydrology: Using genetic programming in conceptual modelling.” 2017. Web. 14 Apr 2021.

Vancouver:

van Ramshorst J(. Genetic Programming in Hydrology: Using genetic programming in conceptual modelling. [Internet] [Thesis]. Delft University of Technology; 2017. [cited 2021 Apr 14]. Available from: http://resolver.tudelft.nl/uuid:e1c648c7-a28f-4a9f-be20-22cc6c2c80eb.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

van Ramshorst J(. Genetic Programming in Hydrology: Using genetic programming in conceptual modelling. [Thesis]. Delft University of Technology; 2017. Available from: http://resolver.tudelft.nl/uuid:e1c648c7-a28f-4a9f-be20-22cc6c2c80eb

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Limerick

13. Hogan, Damien. Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices.

Degree: 2013, University of Limerick

URL: http://hdl.handle.net/10344/4875

►

peer-reviewed

The central hypothesis of this thesis is that it is possible to use a supervised machine learning technique, Genetic Programming (GP), to make accurate… (more)

▼

peer-reviewed

The central hypothesis of this thesis is that it is possible to use a supervised machine learning technique, Genetic Programming (GP), to make accurate predictions and estimations regarding the endurance and retention of multi-level cell NAND Flash Memory devices. The retention of storage locations, or blocks, within these devices is the length of time for which they successfully retain their data, while their endurance is the number of times they can be programmed and erased prior to failure. Manufacturers currently place conservative speci cations on their devices since there is no technique available to quickly determine the actual endurance and retention capabilities of blocks within them. An extensive empirical evaluation of a number of MLC NAND Flash devices is completed, identifying features for use with GP, before expressions are evolved to make predictions and estimations regarding the retention and endurance of blocks. The empirical evaluation highlights the large variations in performance between blocks in di erent devices of the same speci cation, and even between blocks within the same device. As well as building a data set for later use with GP, the durations of program and erase operations are identi ed as features with which to make endurance predictions and estimations, while a relationship between block location and endurance is also established. GP is employed to evolve binary classi cation expressions, referred to as retention period classi ers, to predict whether blocks will correctly retain their data for a speci ed length of time. Following this, endurance classi ers are evolved to predict whether blocks will successfully complete a prede ned number of cycles. Finally, symbolic regression expressions are evolved, building on the earlier experiments, to estimate the actual number of cycles each block will complete prior to failure and are referred to as endurance estimators.

Advisors/Committee Members: Ryan, Conor, Arbuckle, Tom.

Subjects/Keywords: genetic programming; GP; data

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APA (6^th Edition):

Hogan, D. (2013). Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices. (Thesis). University of Limerick. Retrieved from http://hdl.handle.net/10344/4875

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hogan, Damien. “Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices.” 2013. Thesis, University of Limerick. Accessed April 14, 2021. http://hdl.handle.net/10344/4875.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hogan, Damien. “Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices.” 2013. Web. 14 Apr 2021.

Vancouver:

Hogan D. Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices. [Internet] [Thesis]. University of Limerick; 2013. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10344/4875.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hogan D. Genetic programming based predictions and estimations for the endurance and retention of NAND flash memory devices. [Thesis]. University of Limerick; 2013. Available from: http://hdl.handle.net/10344/4875

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Limerick

14. Medernach, David. Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming.

Degree: 2017, University of Limerick

URL: http://hdl.handle.net/10344/6103

►

peer-reviewed

Charles Darwin introduced the theory of evolution by natural selection in [Darwin and Wallace, 1858], and since that time the concept has remained unchanged… (more)

Subjects/Keywords: genetic programming; artificial life; ecosystems

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APA (6^th Edition):

Medernach, D. (2017). Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming. (Thesis). University of Limerick. Retrieved from http://hdl.handle.net/10344/6103

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Medernach, David. “Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming.” 2017. Thesis, University of Limerick. Accessed April 14, 2021. http://hdl.handle.net/10344/6103.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Medernach, David. “Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming.” 2017. Web. 14 Apr 2021.

Vancouver:

Medernach D. Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming. [Internet] [Thesis]. University of Limerick; 2017. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10344/6103.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Medernach D. Comparative study of effects of fitness landscape changes in open-ended evolutionary simulations and in genetic programming. [Thesis]. University of Limerick; 2017. Available from: http://hdl.handle.net/10344/6103

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Ryerson University

15. Jirasek, Lubomir. A genetic algorithm based partition generation and optimization method for finite element problems.

Degree: 2009, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A1387

► A two-step partitioning algorithm for FE meshes is proposed in this work for the purposes of time savings. A direct method based on the concept… (more)

Subjects/Keywords: Genetic algorithms; Genetic programming (Computer science)

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APA (6^th Edition):

Jirasek, L. (2009). A genetic algorithm based partition generation and optimization method for finite element problems. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A1387

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Jirasek, Lubomir. “A genetic algorithm based partition generation and optimization method for finite element problems.” 2009. Thesis, Ryerson University. Accessed April 14, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A1387.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Jirasek, Lubomir. “A genetic algorithm based partition generation and optimization method for finite element problems.” 2009. Web. 14 Apr 2021.

Vancouver:

Jirasek L. A genetic algorithm based partition generation and optimization method for finite element problems. [Internet] [Thesis]. Ryerson University; 2009. [cited 2021 Apr 14]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1387.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jirasek L. A genetic algorithm based partition generation and optimization method for finite element problems. [Thesis]. Ryerson University; 2009. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1387

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Ryerson University

16. Gebremariam, Michael. Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm.

Degree: 2010, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A6075

► The objective of this project is to develop a software tool which assists in comparison of a work known as "M-GenESys: Multi Structure Genetic Algorithm… (more)

Subjects/Keywords: Genetic programming (Computer science); Genetic algorithms

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APA (6^th Edition):

Gebremariam, M. (2010). Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A6075

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Gebremariam, Michael. “Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm.” 2010. Thesis, Ryerson University. Accessed April 14, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A6075.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Gebremariam, Michael. “Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm.” 2010. Web. 14 Apr 2021.

Vancouver:

Gebremariam M. Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm. [Internet] [Thesis]. Ryerson University; 2010. [cited 2021 Apr 14]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A6075.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Gebremariam M. Implementation of integrated design space exploration of scheduling, allocation and binding in high level synthesis using multi structure genetic algorithm. [Thesis]. Ryerson University; 2010. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A6075

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Stirling

17. Oaken, David R. Optimisation of Definition Structures & Parameter Values in Process Algebra Models Using Evolutionary Computation.

Degree: PhD, 2014, University of Stirling

URL: http://hdl.handle.net/1893/21206

► Process Algebras are a Formal Modelling methodology which are an effective tool for defining models of complex systems, particularly those involving multiple interacting processes. However,… (more)

▼ Process Algebras are a Formal Modelling methodology which are an effective tool for defining models of complex systems, particularly those involving multiple interacting processes. However, describing such a model using Process Algebras requires expertise from both the modeller and the domain expert. Finding the correct model to describe a system can be difficult. Further more, even with the correct model, parameter tuning to allow model outputs to match experimental data can also be both difficult and time consuming. Evolutionary Algorithms provide effective methods for finding solutions to optimisation problems with large and noisy search spaces. Evolutionary Algorithms have been proven to be well suited to investigating parameter fitting problems in order to match known data or desired behaviour. It is proposed that Process Algebras and Evolutionary Algorithms have complementary strengths for developing models of complex systems. Evolutionary Algorithms require a precise and accurate fitness function to score and rank solutions. Process Algebras can be incorporated into the fitness function to provide this mathematical score. Presented in this work is the Evolving Process Algebra (EPA) framework, designed for the application of Evolutionary Algorithms (specifically Genetic Algorithms and Genetic Programming optimisation techniques) to models described in Process Algebra (specifically PEPA and Bio-PEPA) with the aim of evolving fitter models. The EPA framework is demonstrated using multiple complex systems. For PEPA this includes the dining philosophers resource allocation problem, the repressilator genetic circuit, the G-protein cellular signal regulators and two epidemiological problems: HIV and the measles virus. For Bio-PEPA the problems include a biochemical reactant-product system, a generic genetic network, a variant of the G-protein system and three epidemiological problems derived from the measles virus. Also presented is the EPA Utility Assistant program; a lightweight graphical user interface. This is designed to open the full functionality and parallelisation of the EPA framework to beginner or naive users. In addition, the assistant program aids in collating and graphing after experiments are completed.

Subjects/Keywords: genetic algorithms; process algebra; genetic programming; optimisation; Genetic algorithms; Algebra; Genetic programming (Computer science); Optimisation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Oaken, D. R. (2014). Optimisation of Definition Structures & Parameter Values in Process Algebra Models Using Evolutionary Computation. (Doctoral Dissertation). University of Stirling. Retrieved from http://hdl.handle.net/1893/21206

Chicago Manual of Style (16^th Edition):

Oaken, David R. “Optimisation of Definition Structures & Parameter Values in Process Algebra Models Using Evolutionary Computation.” 2014. Doctoral Dissertation, University of Stirling. Accessed April 14, 2021. http://hdl.handle.net/1893/21206.

MLA Handbook (7^th Edition):

Oaken, David R. “Optimisation of Definition Structures & Parameter Values in Process Algebra Models Using Evolutionary Computation.” 2014. Web. 14 Apr 2021.

Vancouver:

Oaken DR. Optimisation of Definition Structures & Parameter Values in Process Algebra Models Using Evolutionary Computation. [Internet] [Doctoral dissertation]. University of Stirling; 2014. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/1893/21206.

Council of Science Editors:

Oaken DR. Optimisation of Definition Structures & Parameter Values in Process Algebra Models Using Evolutionary Computation. [Doctoral Dissertation]. University of Stirling; 2014. Available from: http://hdl.handle.net/1893/21206

University of KwaZulu-Natal

18. Igwe, Kevin Chizoba. A study of genetic programming and grammatical evolution for automatic object-oriented programming.

Degree: 2016, University of KwaZulu-Natal

URL: http://hdl.handle.net/10413/14500

► Manual programming is time consuming and challenging for a complex problem. For efficiency of the manual programming process, human programmers adopt the object-oriented approach to… (more)

Subjects/Keywords: Genetic programming.; Grammatical evolution.; Automatic programming.; Object-oriented programming.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Igwe, K. C. (2016). A study of genetic programming and grammatical evolution for automatic object-oriented programming. (Thesis). University of KwaZulu-Natal. Retrieved from http://hdl.handle.net/10413/14500

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Igwe, Kevin Chizoba. “A study of genetic programming and grammatical evolution for automatic object-oriented programming.” 2016. Thesis, University of KwaZulu-Natal. Accessed April 14, 2021. http://hdl.handle.net/10413/14500.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Igwe, Kevin Chizoba. “A study of genetic programming and grammatical evolution for automatic object-oriented programming.” 2016. Web. 14 Apr 2021.

Vancouver:

Igwe KC. A study of genetic programming and grammatical evolution for automatic object-oriented programming. [Internet] [Thesis]. University of KwaZulu-Natal; 2016. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10413/14500.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Igwe KC. A study of genetic programming and grammatical evolution for automatic object-oriented programming. [Thesis]. University of KwaZulu-Natal; 2016. Available from: http://hdl.handle.net/10413/14500

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

19. Minařík, Miloš. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/189849

► During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make… (more)

Subjects/Keywords: evoluce; genetické programování; kartézské genetické programování; sebemodifikující kartézské genetické programování; evolution; genetic programming; cartesian genetic programming; self-modifying cartesian genetic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Minařík, M. (2020). Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189849

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Minařík, Miloš. “Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.” 2020. Thesis, Brno University of Technology. Accessed April 14, 2021. http://hdl.handle.net/11012/189849.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Minařík, Miloš. “Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.” 2020. Web. 14 Apr 2021.

Vancouver:

Minařík M. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/11012/189849.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Minařík M. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189849

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

20. Minařík, Miloš. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/188681

► During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make… (more)

Subjects/Keywords: evoluce; genetické programování; kartézské genetické programování; sebemodifikující kartézské genetické programování; evolution; genetic programming; cartesian genetic programming; self-modifying cartesian genetic programming

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Minařík, M. (2020). Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/188681

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Minařík, Miloš. “Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.” 2020. Thesis, Brno University of Technology. Accessed April 14, 2021. http://hdl.handle.net/11012/188681.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Minařík, Miloš. “Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.” 2020. Web. 14 Apr 2021.

Vancouver:

Minařík M. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/11012/188681.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Minařík M. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/188681

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology

21. Minařík, Miloš. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.

Degree: 2020, Brno University of Technology

URL: http://hdl.handle.net/11012/54257

► During the last years cartesian genetic programming proved to be a very perspective area of the evolutionary computing. However it has its limitations, which make… (more)

Subjects/Keywords: evoluce; genetické programování; kartézské genetické programování; sebemodifikující kartézské genetické programování; evolution; genetic programming; cartesian genetic programming; self-modifying cartesian genetic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Minařík, M. (2020). Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/54257

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Minařík, Miloš. “Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.” 2020. Thesis, Brno University of Technology. Accessed April 14, 2021. http://hdl.handle.net/11012/54257.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Minařík, Miloš. “Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming.” 2020. Web. 14 Apr 2021.

Vancouver:

Minařík M. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/11012/54257.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Minařík M. Sebemodifikující se programy v kartézském genetickém programování: Self-Modifying Programs in Cartesian Genetic Programming. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/54257

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Dalhousie University

22. Atwater, Aaron. Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data.

Degree: Master of Computer Science, Faculty of Computer Science, 2013, Dalhousie University

URL: http://hdl.handle.net/10222/35358

►

Hyperref'd copy available at: https://web.cs.dal.ca/~atwater/

Classification under streaming data constraints implies that training must be performed continuously, can only access individual exemplars for a short… (more)

Subjects/Keywords: computer science; genetic programming; machine learning; classification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Atwater, A. (2013). Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data. (Masters Thesis). Dalhousie University. Retrieved from http://hdl.handle.net/10222/35358

Chicago Manual of Style (16^th Edition):

Atwater, Aaron. “Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data.” 2013. Masters Thesis, Dalhousie University. Accessed April 14, 2021. http://hdl.handle.net/10222/35358.

MLA Handbook (7^th Edition):

Atwater, Aaron. “Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data.” 2013. Web. 14 Apr 2021.

Vancouver:

Atwater A. Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data. [Internet] [Masters thesis]. Dalhousie University; 2013. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10222/35358.

Council of Science Editors:

Atwater A. Towards Coevolutionary Genetic Programming with Pareto Archiving Under Streaming Data. [Masters Thesis]. Dalhousie University; 2013. Available from: http://hdl.handle.net/10222/35358

Dalhousie University

23. Rahimi, Sara. Label Free Change Detection on Streaming Data with Cooperative Multi-objective Genetic Programming.

Degree: Master of Computer Science, Faculty of Computer Science, 2013, Dalhousie University

URL: http://hdl.handle.net/10222/35420

► Classification under streaming data conditions requires that the machine learning approach operate interactively with the stream content. Thus, given some initial machine learning classification capability,… (more)

Subjects/Keywords: Change Detection; Streaming Data; Genetic Programming

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APA (6^th Edition):

Rahimi, S. (2013). Label Free Change Detection on Streaming Data with Cooperative Multi-objective Genetic Programming. (Masters Thesis). Dalhousie University. Retrieved from http://hdl.handle.net/10222/35420

Chicago Manual of Style (16^th Edition):

Rahimi, Sara. “Label Free Change Detection on Streaming Data with Cooperative Multi-objective Genetic Programming.” 2013. Masters Thesis, Dalhousie University. Accessed April 14, 2021. http://hdl.handle.net/10222/35420.

MLA Handbook (7^th Edition):

Rahimi, Sara. “Label Free Change Detection on Streaming Data with Cooperative Multi-objective Genetic Programming.” 2013. Web. 14 Apr 2021.

Vancouver:

Rahimi S. Label Free Change Detection on Streaming Data with Cooperative Multi-objective Genetic Programming. [Internet] [Masters thesis]. Dalhousie University; 2013. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10222/35420.

Council of Science Editors:

Rahimi S. Label Free Change Detection on Streaming Data with Cooperative Multi-objective Genetic Programming. [Masters Thesis]. Dalhousie University; 2013. Available from: http://hdl.handle.net/10222/35420

24. Brameier, Markus. On linear genetic programming.

Degree: 2004, Universität Dortmund

URL: http://hdl.handle.net/2003/20098

► The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to… (more)

▼ The thesis is about linear genetic programming (LGP), a machine learning approach that evolves computer programs as sequences of imperative instructions. Two fundamental differences to the more commontree-based variant (TGP) may be identified. These are the graph-based functional structure of linear genetic programs, on the one hand, and the existence of structurally noneffective code, on the other hand.The two major objectives of this work comprise(1) the development of more advanced methods and variation operators to produce better and more compact program solutions and (2) the analysis of general EA/GP phenomena in linear GP, including intron code, neutral variations, and code growth, among others.First, we introduce efficient algorithms for extracting features of the imperative and functional structure of linear genetic programs.In doing so, especially the detection and elimination of noneffective code during runtime will turn out as a powerful tool to accelerate the time-consuming step of fitness evaluation in GP.Variation operators are discussed systematically for the linear program representation. We will demonstrate that so called effective instruction mutations achieve the best performance in terms of solution quality.These mutations operate only on the (structurally) effective codeand restrict the mutation step size to one instruction.One possibility to further improve their performance is to explicitly increase the probability of neutral variations. As a second, more time-efficient alternative we explicitly controlthe mutation step size on the effective code (effective step size).Minimum steps do not allow more than one effective instruction to change its effectiveness status. That is, only a single node may beconnected to or disconnected from the effective graph component. It is an interesting phenomenon that, to some extent, the effective code becomes more robust against destructions over the generations already implicitly. A special concern of this thesis is to convince the reader that thereare some serious arguments for using a linear representation.In a crossover-based comparison LGP has been found superior to TGPover a set of benchmark problems. Furthermore, linear solutions turned out to be more compact than tree solutions due to (1) multiple usage of subgraph results and (2) implicit parsimony pressure by structurally noneffective code.The phenomenon of code growth is analyzed for different lineargenetic operators. When applying instruction mutations exclusivelyalmost only neutral variations may be held responsible for the emergence and propagation of intron code. It is noteworthy that linear geneticprograms may not grow if all neutral variation effects are rejected and if the variation step size is minimum.For the same reasons effective instruction mutations realize an implicit complexity control in linear GP which reduces a possible negative effect of code growth to a minimum.Another noteworthy result in this context is that program size is strongly increased by crossover while it is hardly… Advisors/Committee Members: Banzhaf, Wolfgang.

Subjects/Keywords: Evolutionary algorithms; Genetic programming; Machine learning; 004

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APA (6^th Edition):

Brameier, M. (2004). On linear genetic programming. (Thesis). Universität Dortmund. Retrieved from http://hdl.handle.net/2003/20098

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Brameier, Markus. “On linear genetic programming.” 2004. Thesis, Universität Dortmund. Accessed April 14, 2021. http://hdl.handle.net/2003/20098.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Brameier, Markus. “On linear genetic programming.” 2004. Web. 14 Apr 2021.

Vancouver:

Brameier M. On linear genetic programming. [Internet] [Thesis]. Universität Dortmund; 2004. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/2003/20098.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Brameier M. On linear genetic programming. [Thesis]. Universität Dortmund; 2004. Available from: http://hdl.handle.net/2003/20098

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Ryerson University

25. Mahvarsayyad, Fereshteh. Texture classification using gene expression programming.

Degree: 2009, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A1873

► In computer vision, segmentation refers to the process of subdividing a digital image into constituent regions with homogeneity in some image characteristics. Image segmentation is… (more)

Subjects/Keywords: Genetic programming (Computer science); Visual texture recognition

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APA (6^th Edition):

Mahvarsayyad, F. (2009). Texture classification using gene expression programming. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A1873

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Mahvarsayyad, Fereshteh. “Texture classification using gene expression programming.” 2009. Thesis, Ryerson University. Accessed April 14, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A1873.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Mahvarsayyad, Fereshteh. “Texture classification using gene expression programming.” 2009. Web. 14 Apr 2021.

Vancouver:

Mahvarsayyad F. Texture classification using gene expression programming. [Internet] [Thesis]. Ryerson University; 2009. [cited 2021 Apr 14]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1873.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Mahvarsayyad F. Texture classification using gene expression programming. [Thesis]. Ryerson University; 2009. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A1873

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Victoria University of Wellington

26. Ahmed, Soha. Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data.

Degree: 2015, Victoria University of Wellington

URL: http://hdl.handle.net/10063/4913

► Mass spectrometry (MS) is currently the most commonly used technology in biochemical research for proteomic analysis. The primary goal of proteomic profiling using mass spectrometry… (more)

▼ Mass spectrometry (MS) is currently the most commonly used technology in biochemical research for proteomic analysis. The primary goal of proteomic profiling using mass spectrometry is the classification of samples from different experimental states. To classify the MS samples, the identification of protein or peptides (biomarker detection) that are expressed differently between the classes, is required. However, due to the high dimensionality of the data and the small number of samples, classification of MS data is extremely challenging. Another important aspect of biomarker detection is the verification of the detected biomarker that acts as an intermediate step before passing these biomarkers to the experimental validation stage. Biomarker detection aims at altering the input space of the learning algorithm for improving classification of proteomic or metabolomic data. This task is performed through feature manipulation. Feature manipulation consists of three aspects: feature ranking, feature selection, and feature construction. Genetic programming (GP) is an evolutionary computation algorithm that has the intrinsic capability for the three aspects of feature manipulation. The ability of GP for feature manipulation in proteomic biomarker discovery has not been fully investigated. This thesis, therefore, proposes an embedded methodology for these three aspects of feature manipulation in high dimensional MS data using GP. The thesis also presents a method for biomarker verification, using GP. The thesis investigates the use of GP for both single-objective and multi-objective feature selection and construction. In feature ranking, the thesis proposes a GP-based method for ranking subsets of features by using GP as an ensemble approach. The proposed algorithm uses GP capability to combine the advantages of different feature ranking metrics and evolve a new ranking scheme for the subset of the features selected from the top ranked features. The capability of GP as a classifier is also investigated by this method. The results show that GP can select a smaller number of features and provide a better ranking of the selected features, which can improve the classification performance of five classifiers. In feature construction, this thesis proposes a novel multiple feature construction method, which uses a single GP tree to generate a new set of high-level features from the original set of selected features. The results show that the proposed new algorithm outperforms two feature selection algorithms. In feature selection, the thesis introduces the first GP multi-objective method for biomarker detection, which simultaneously increase the classification accuracy and reduce the number of detected features. The proposed multi-objective method can obtain better subsets of features than the single-objective algorithm and two traditional multi-objective approaches for feature selection. This thesis also develops the first multi-objective multiple feature construction algorithm for MS data. The proposed method aims at both… Advisors/Committee Members: Zhang, Mengjie, Peng, Lifeng.

Subjects/Keywords: Biomarker detection; Mass spectrometry; Genetic programming

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Ahmed, S. (2015). Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/4913

Chicago Manual of Style (16^th Edition):

Ahmed, Soha. “Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data.” 2015. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/4913.

MLA Handbook (7^th Edition):

Ahmed, Soha. “Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data.” 2015. Web. 14 Apr 2021.

Vancouver:

Ahmed S. Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2015. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/4913.

Council of Science Editors:

Ahmed S. Genetic Programming for Biomarker Detection in Classification of Mass Spectrometry Data. [Doctoral Dissertation]. Victoria University of Wellington; 2015. Available from: http://hdl.handle.net/10063/4913

University of Waikato

27. Liu, Liang. Linear Genetic Programming with Experience .

Degree: 2015, University of Waikato

URL: http://hdl.handle.net/10289/9762

► A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Genetic Programming (LGP) is studied. In this study, structures used… (more)

▼ A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Genetic Programming (LGP) is studied. In this study, structures used to organize the trained ML models are called Experience Models (EM). They are used for different mutate actions of the mutation operator in LGP. The purpose of using EM is to regulate the random search performed by the mutation operator. The aim of using EMs is to let the suitable candidates have higher chances to be selected. In this study, two sources of knowledge are used to create the training sets that are used to train ML models. The first source is the pre-existing knowledge of symbolic regression. This knowledge reflects the effect of adding one math function segment to another math function segment. The second source is the knowledge generated during the evolution of LGP. This knowledge reflects the effect of using different gene components at different chromosome indexes on the overall fitness. Based on these two sources of knowledge, two types of EM are designed. They are Static Model (SM) and Dynamic Model (DM). The SM uses ML models trained with the first knowledge source. A SM tries to achieve the aim of using an EM by reducing the size of the candidate sets used by the increase action of the mutation operator. The DM uses ML models trained with the second knowledge source. A DM tries to achieve the aim of using an EM by creating distributions of gene component types, which can reflect the information in the second knowledge source, for change action of the mutation operator. In this study, SM is used only for increase action in the mutation operator; DM is used only for change action in the mutation operator. From the experiment results, if compared with a LGP, when a LGP using a SM, it tends to need fewer generations to have a hit, at the same time achieving similar mean best fitness. In contrary, when used with a DM, a LGP do not show performance improvements. Advisors/Committee Members: Mayo, Michael (advisor).

Subjects/Keywords: Linear Genetic Programming; Machine Learning; Symbolic Regression

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Liu, L. (2015). Linear Genetic Programming with Experience . (Masters Thesis). University of Waikato. Retrieved from http://hdl.handle.net/10289/9762

Chicago Manual of Style (16^th Edition):

Liu, Liang. “Linear Genetic Programming with Experience .” 2015. Masters Thesis, University of Waikato. Accessed April 14, 2021. http://hdl.handle.net/10289/9762.

MLA Handbook (7^th Edition):

Liu, Liang. “Linear Genetic Programming with Experience .” 2015. Web. 14 Apr 2021.

Vancouver:

Liu L. Linear Genetic Programming with Experience . [Internet] [Masters thesis]. University of Waikato; 2015. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10289/9762.

Council of Science Editors:

Liu L. Linear Genetic Programming with Experience . [Masters Thesis]. University of Waikato; 2015. Available from: http://hdl.handle.net/10289/9762

Hong Kong University of Science and Technology

28. Zhao, Shenyang. Exhaustive search by division of solution space into subspaces applied to genetic algorithms and genetic programming.

Degree: 2011, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-7159 ; https://doi.org/10.14711/thesis-b1136617 ; http://repository.ust.hk/ir/bitstream/1783.1-7159/1/th_redirect.html

► By dividing the solution space into several subspaces and performing search restricted to individual subspace has the advantage that effort in one subspace will not… (more)

Subjects/Keywords: Genetic algorithms ; Programming (Mathematics) ; Evolutionary computation

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APA (6^th Edition):

Zhao, S. (2011). Exhaustive search by division of solution space into subspaces applied to genetic algorithms and genetic programming. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-7159 ; https://doi.org/10.14711/thesis-b1136617 ; http://repository.ust.hk/ir/bitstream/1783.1-7159/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhao, Shenyang. “Exhaustive search by division of solution space into subspaces applied to genetic algorithms and genetic programming.” 2011. Thesis, Hong Kong University of Science and Technology. Accessed April 14, 2021. http://repository.ust.hk/ir/Record/1783.1-7159 ; https://doi.org/10.14711/thesis-b1136617 ; http://repository.ust.hk/ir/bitstream/1783.1-7159/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhao, Shenyang. “Exhaustive search by division of solution space into subspaces applied to genetic algorithms and genetic programming.” 2011. Web. 14 Apr 2021.

Vancouver:

Zhao S. Exhaustive search by division of solution space into subspaces applied to genetic algorithms and genetic programming. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2011. [cited 2021 Apr 14]. Available from: http://repository.ust.hk/ir/Record/1783.1-7159 ; https://doi.org/10.14711/thesis-b1136617 ; http://repository.ust.hk/ir/bitstream/1783.1-7159/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhao S. Exhaustive search by division of solution space into subspaces applied to genetic algorithms and genetic programming. [Thesis]. Hong Kong University of Science and Technology; 2011. Available from: http://repository.ust.hk/ir/Record/1783.1-7159 ; https://doi.org/10.14711/thesis-b1136617 ; http://repository.ust.hk/ir/bitstream/1783.1-7159/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Victoria University of Wellington

29. Kinzett, Alan David. Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming.

Degree: 2011, Victoria University of Wellington

URL: http://hdl.handle.net/10063/1863

► In tree-based genetic programming (GP) there is a tendency for the program trees to increase in size from one generation to the next. If this… (more)

▼ In tree-based genetic programming (GP) there is a tendency for the program trees to increase in size from one generation to the next. If this increase in program size is not accompanied by an improvement in fitness then this unproductive increase is known as bloat. It is standard practice to place some form of control on program size. This can be done by limiting the number of nodes or the depth of the program trees, or by adding a component to the fitness function that rewards smaller programs (parsimony pressure) or by simplifying individual programs using algebraic methods. This thesis proposes a novel program simplification method called numerical simplification that uses only the range of values the nodes take during fitness evaluation. The effect of online program simplification, both algebraic and numerical, on program size and resource usage is examined. This thesis also examines the distribution of program fragments within a genetic programming population and how this is changed by using simplification. It is shown that both simplification approaches result in reductions in average program size, memory used and computation time and that numerical simplification performs at least as well as algebraic simplification, and in some cases will outperform algebraic simplification. This reduction in program size and the resources required to process the GP run come without any significant reduction in accuracy. It is also shown that although the two online simplification methods destroy some existing program fragments, they generate new fragments during evolution, which compensates for any negative effects from the disruption of existing fragments. It is also shown that, after the first few generations, the rate new fragments are created, the rate fragments are lost from the population, and the number of distinct (different) fragments in the population remain within a very narrow range of values for the remainder of the run. Advisors/Committee Members: Zhang, Mengjie.

Subjects/Keywords: Computational intelligence; Genetic programming; Distributed artificial intelligence

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APA (6^th Edition):

Kinzett, A. D. (2011). Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/1863

Chicago Manual of Style (16^th Edition):

Kinzett, Alan David. “Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming.” 2011. Masters Thesis, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/1863.

MLA Handbook (7^th Edition):

Kinzett, Alan David. “Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming.” 2011. Web. 14 Apr 2021.

Vancouver:

Kinzett AD. Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming. [Internet] [Masters thesis]. Victoria University of Wellington; 2011. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/1863.

Council of Science Editors:

Kinzett AD. Numerical Simplification and its Effect on Fragment Distributions in Genetic Programming. [Masters Thesis]. Victoria University of Wellington; 2011. Available from: http://hdl.handle.net/10063/1863

Victoria University of Wellington

30. Karunakaran, Deepak. Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment.

Degree: 2019, Victoria University of Wellington

URL: http://hdl.handle.net/10063/8614

► Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different… (more)

▼ Scheduling is an important problem in artificial intelligence and operations research. In production processes, it deals with the problem of allocation of resources to different tasks with the goal of optimizing one or more objectives. Job shop scheduling is a classic and very common scheduling problem. In the real world, shop environments dynamically change due to events such as the arrival of new jobs and machine breakdown. In such manufacturing environments, uncertainty in shop parameters is typical. It is of vital importance to develop methods for effective scheduling in such practical settings. Scheduling using heuristics like dispatching rules is very popular and suitable for such environments due to their low computational cost and ease of implementation. For a dynamic manufacturing environment with varying shop scenarios, using a universal dispatching rule is not very effective. But manual development of effective dispatching rules is difficult, time consuming and requires expertise. Genetic programming is an evolutionary approach which is suitable for automatically designing effective dispatching rules. Since the genetic programming approach searches in the space of heuristics (dispatching rules) instead of building up a schedule, it is considered a hyper-heuristic approach. Genetic programming like many other evolutionary approaches is computationally expensive. Therefore, it is of vital importance to present the genetic programming based hyper-heuristic (GPHH) system with scheduling problem instances which capture the complex shop scenarios capturing the difficulty in scheduling. Active learning is a related concept from machine learning which concerns with effective sampling of those training instances to promote the accuracy of the learned model. The overall goal of this thesis is to develop effective and efficient genetic programming based hyper-heuristic approaches using active learning techniques for dynamic job shop scheduling problems with one or more objectives. This thesis develops new representations for genetic programming enabling it to incorporate the uncertainty information about processing times of the jobs. Furthermore, a cooperative co-evolutionary approach is developed for GPHH which evolves a pair of dispatching rules for bottleneck and non-bottleneck machines in the dynamic environment with uncertainty in processing times arising due to varying machine characteristics. The results show that the new representations and training approaches are able to significantly improve the performance of evolved dispatching rules. This thesis develops a new GPHH framework in order to incorporate active learning methods toward sampling DJSS instances which promote the evolution of more effective rules. Using this framework, two new active sampling methods were developed to identify those scheduling problem instances which promoted evolution of effective dispatching rules. The results show the advantages of using active learning methods for scheduling under the purview of GPHH. This thesis… Advisors/Committee Members: Zhang, Mengjie, Mei, Yi, Chen, Aaron.

Subjects/Keywords: Online scheduling; Genetic programming; Active learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Karunakaran, D. (2019). Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment. (Doctoral Dissertation). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/8614

Chicago Manual of Style (16^th Edition):

Karunakaran, Deepak. “Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment.” 2019. Doctoral Dissertation, Victoria University of Wellington. Accessed April 14, 2021. http://hdl.handle.net/10063/8614.

MLA Handbook (7^th Edition):

Karunakaran, Deepak. “Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment.” 2019. Web. 14 Apr 2021.

Vancouver:

Karunakaran D. Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment. [Internet] [Doctoral dissertation]. Victoria University of Wellington; 2019. [cited 2021 Apr 14]. Available from: http://hdl.handle.net/10063/8614.

Council of Science Editors:

Karunakaran D. Active Learning Methods for Dynamic Job Shop Scheduling using Genetic Programming under Uncertain Environment. [Doctoral Dissertation]. Victoria University of Wellington; 2019. Available from: http://hdl.handle.net/10063/8614

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Open Access Theses and Dissertations