subject:(Genetic algorithms)

1. Satishkumar, M V. Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms.

Degree: 2010, Jawaharlal Nehru Technological University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/2255

► High degree of flexibility and quick response times have become essential features of modern manufacturing systems where customers are demanding for a variety of products… (more)

Subjects/Keywords: Genetic Algorithms

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APA (6^th Edition):

Satishkumar, M. V. (2010). Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms. (Thesis). Jawaharlal Nehru Technological University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/2255

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):

Satishkumar, M V. “Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms.” 2010. Thesis, Jawaharlal Nehru Technological University. Accessed February 23, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/2255.

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

MLA Handbook (7^th Edition):

Satishkumar, M V. “Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms.” 2010. Web. 23 Feb 2020.

Vancouver:

Satishkumar MV. Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms. [Internet] [Thesis]. Jawaharlal Nehru Technological University; 2010. [cited 2020 Feb 23]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/2255.

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

Council of Science Editors:

Satishkumar MV. Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms. [Thesis]. Jawaharlal Nehru Technological University; 2010. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/2255

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

Brunel University

2. Saud, Moudar. Convolution based real-time control strategy for vehicle active suspension systems.

Degree: PhD, 2009, Brunel University

URL: http://bura.brunel.ac.uk/handle/2438/6278 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549629

► A novel real-time control method that minimises linear system vibrations when it is subjected to an arbitrary external excitation is proposed in this study. The… (more)

▼ A novel real-time control method that minimises linear system vibrations when it is subjected to an arbitrary external excitation is proposed in this study. The work deals with a discrete differential dynamic programming type of problem, in which an external disturbance is controlled over a time horizon by a control force strategy constituted by the well-known convolution approach. The proposed method states that if a control strategy can be established to restore an impulse external disturbance, then the convolution concept can be used to generate an overall control strategy to control the system response when it is subjected to an arbitrary external disturbance. The arbitrary disturbance is divided into impulses and by simply scaling, shifting and summation of the obtained control strategy against the impulse input for each impulse of the arbitrary disturbance, the overall control strategy will be established. Genetic Algorithm was adopted to obtain an optimal control force plan to suppress the system vibrations when it is subjected to a shock disturbance, and then the Convolution concept was used to enable the system response to be controlled in real-time using the obtained control strategy. Numerical tests were carried out on a two-degree of freedom quarter-vehicle active suspension model and the results were compared with results generated using the Linear Quadratic Regulator (LQR) method. The method was also applied to control the vibration of a seven-degree of freedom full-vehicle active suspension model. In addition, the effect of a time delay on the performance of the proposed approach was also studied. To demonstrate the applicability of the proposed method in real-time control, experimental tests were performed on a quarter-vehicle test rig equipped with a pneumatic active suspension. Numerical and experimental results showed the effectiveness of the proposed method in reducing the vehicle vibrations. One of the main contributions of this work besides using the Convolution concept to provide a real time control strategy is the reduction in the number of sensors needed to construct the proposed method as the disturbance amplitude is the only parameter needed to be measured (known). Finally, having achieved what has been proposed above, a generic robust control method is accomplished, which not only can be applied for active suspension systems but also in many other fields.

Subjects/Keywords: 629.2; Genetic algorithms

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APA (6^th Edition):

Saud, M. (2009). Convolution based real-time control strategy for vehicle active suspension systems. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/6278 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549629

Chicago Manual of Style (16^th Edition):

Saud, Moudar. “Convolution based real-time control strategy for vehicle active suspension systems.” 2009. Doctoral Dissertation, Brunel University. Accessed February 23, 2020. http://bura.brunel.ac.uk/handle/2438/6278 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549629.

MLA Handbook (7^th Edition):

Saud, Moudar. “Convolution based real-time control strategy for vehicle active suspension systems.” 2009. Web. 23 Feb 2020.

Vancouver:

Saud M. Convolution based real-time control strategy for vehicle active suspension systems. [Internet] [Doctoral dissertation]. Brunel University; 2009. [cited 2020 Feb 23]. Available from: http://bura.brunel.ac.uk/handle/2438/6278 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549629.

Council of Science Editors:

Saud M. Convolution based real-time control strategy for vehicle active suspension systems. [Doctoral Dissertation]. Brunel University; 2009. Available from: http://bura.brunel.ac.uk/handle/2438/6278 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549629

Ryerson University

3. Hajipour, Yassin. Non-periodic inspection of optimization of repairable systems.

Degree: 2016, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A5215

► This study proposes models to find the optimal non-periodic inspection interval over a finite planning horizon for two types of multi-component repairable systems. The first… (more)

▼ This study proposes models to find the optimal non-periodic inspection interval over a finite planning horizon for two types of multi-component repairable systems. The first system consists of hard-type and soft-type components, and the second system is a k-out-of-m system with m identical components. The failures of components in both systems follow a non-homogeneous Poisson process. The failure of soft-type components and the failure of components in a k-out-of-m system when the number of failed components is still less than m-k+1, are soft failures. Soft failures are revealed only at scheduled inspections or when an event of opportunistic inspection or a system failure occurs. The failures of hard-type components or the failure of (m-k+1)th failed component in a k-out-of-m system are hard failures, and cause the system to stop functioning. Hard failures are revealed immediately and the failed components are fixed. In this study, a failed component is either replaced or minimally repaired according to its age at failure time. To find the optimal inspection schedules for the systems, we minimize the total expected cost of the systems over a finite planning horizon. The total cost for the first type of system includes the costs of components’ minimal repairs, replacements, downtimes, and the scheduled inspections. The total cost of a k-out-of-m system has an additional penalty cost for system failures. We consider a binary variable for a possible scheduled inspection’s time, in which 1 indicates performing a planned inspection at that time, and 0 shows no inspection to be performed. Thus, our goal is to find the optimal vector of binary decision variables which results in the minimum total cost of the system. A recursive formula is developed to calculate the expected number of minimal repairs, replacements and downtime of soft-type components. However since obtaining the expected values from the mathematical formula is cumbersome, we develop a simulation model to obtain the total expected cost for a given non-periodic inspection scheme. We then integrate the simulation model with a genetic algorithm to obtain the optimal inspection scheme.

Subjects/Keywords: Inspections; Genetic algorithms

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APA (6^th Edition):

Hajipour, Y. (2016). Non-periodic inspection of optimization of repairable systems. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A5215

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):

Hajipour, Yassin. “Non-periodic inspection of optimization of repairable systems.” 2016. Thesis, Ryerson University. Accessed February 23, 2020. https://digital.library.ryerson.ca/islandora/object/RULA%3A5215.

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

MLA Handbook (7^th Edition):

Hajipour, Yassin. “Non-periodic inspection of optimization of repairable systems.” 2016. Web. 23 Feb 2020.

Vancouver:

Hajipour Y. Non-periodic inspection of optimization of repairable systems. [Internet] [Thesis]. Ryerson University; 2016. [cited 2020 Feb 23]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A5215.

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

Council of Science Editors:

Hajipour Y. Non-periodic inspection of optimization of repairable systems. [Thesis]. Ryerson University; 2016. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A5215

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

Montana State University

4. Richter, James Neal. On mutation and crossover in the theory of evolutionary algorithms.

Degree: College of Engineering, 2010, Montana State University

URL: https://scholarworks.montana.edu/xmlui/handle/1/2132

► The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied… (more)

Subjects/Keywords: Genetic algorithms.; Dynamics.

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APA (6^th Edition):

Richter, J. N. (2010). On mutation and crossover in the theory of evolutionary algorithms. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/2132

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):

Richter, James Neal. “On mutation and crossover in the theory of evolutionary algorithms.” 2010. Thesis, Montana State University. Accessed February 23, 2020. https://scholarworks.montana.edu/xmlui/handle/1/2132.

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

MLA Handbook (7^th Edition):

Richter, James Neal. “On mutation and crossover in the theory of evolutionary algorithms.” 2010. Web. 23 Feb 2020.

Vancouver:

Richter JN. On mutation and crossover in the theory of evolutionary algorithms. [Internet] [Thesis]. Montana State University; 2010. [cited 2020 Feb 23]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/2132.

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

Council of Science Editors:

Richter JN. On mutation and crossover in the theory of evolutionary algorithms. [Thesis]. Montana State University; 2010. Available from: https://scholarworks.montana.edu/xmlui/handle/1/2132

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

University of Western Australia

5. Czarn, Andrew Simon Timothy. Statistical exploratory analysis of genetic algorithms.

Degree: PhD, 2008, University of Western Australia

URL: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=9580&local_base=GEN01-INS01

► [Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters… (more)

▼ [Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters and how performance varies with respect to changes in parameters. This thesis presents a rigorous yet practical statistical methodology for the exploratory study of GAs. This methodology addresses the issues of experimental design, blocking, power and response curve analysis. It details how statistical analysis may assist the investigator along the exploratory pathway. The statistical methodology is demonstrated in this thesis using a number of case studies with a classical genetic algorithm with one-point crossover and bit-replacement mutation. In doing so we answer a number of questions about the relationship between the performance of the GA and the operators and encoding used. The methodology is suitable, however, to be applied to other adaptive optimization algorithms not treated in this thesis. In the first instance, as an initial demonstration of our methodology, we describe case studies using four standard test functions. It is found that the effect upon performance of crossover is predominantly linear while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behaviour. In the case of crossover both positive and negative gradients are found which suggests using rates as high as possible for some problems while possibly excluding it for others. .... This is illustrated by showing how the use of Gray codes impedes the performance on a lower modality test function compared with a higher modality test function. Computer animation is then used to illustrate the actual mechanism by which this occurs. Fourthly, the traditional concept of a GA is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems an exploration is made by comparing two test problem suites, one comprising non-rotated functions and the other comprising the same functions rotated by 45 degrees in the solution space rendering them not-linear-separable. It is shown that for the difficult rotated functions the crossover operator is detrimental to the performance of the GA. It is conjectured that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Furthermore, the GA was tested on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. It is demonstrated that they match and that the features which make…

Subjects/Keywords: Genetic algorithms; Computer algorithms; Statistical analysis

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

APA (6^th Edition):

Czarn, A. S. T. (2008). Statistical exploratory analysis of genetic algorithms. (Doctoral Dissertation). University of Western Australia. Retrieved from http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=9580&local_base=GEN01-INS01

Chicago Manual of Style (16^th Edition):

Czarn, Andrew Simon Timothy. “Statistical exploratory analysis of genetic algorithms.” 2008. Doctoral Dissertation, University of Western Australia. Accessed February 23, 2020. http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=9580&local_base=GEN01-INS01.

MLA Handbook (7^th Edition):

Czarn, Andrew Simon Timothy. “Statistical exploratory analysis of genetic algorithms.” 2008. Web. 23 Feb 2020.

Vancouver:

Czarn AST. Statistical exploratory analysis of genetic algorithms. [Internet] [Doctoral dissertation]. University of Western Australia; 2008. [cited 2020 Feb 23]. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=9580&local_base=GEN01-INS01.

Council of Science Editors:

Czarn AST. Statistical exploratory analysis of genetic algorithms. [Doctoral Dissertation]. University of Western Australia; 2008. Available from: http://repository.uwa.edu.au:80/R/?func=dbin-jump-full&object_id=9580&local_base=GEN01-INS01

University of Georgia

6. Wu, Lei. A comparison of nature inspired intelligent optimization methods in aerial spray deposition management.

Degree: MS, Artificial Intelligence, 2002, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/wu_lei_200212_ms

► The AGDISP aerial spray simulation model is used to predict the deposition of spray material released from an aircraft. Determining the optimal input values to… (more)

Subjects/Keywords: Genetic Algorithms

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APA (6^th Edition):

Wu, L. (2002). A comparison of nature inspired intelligent optimization methods in aerial spray deposition management. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/wu_lei_200212_ms

Chicago Manual of Style (16^th Edition):

Wu, Lei. “A comparison of nature inspired intelligent optimization methods in aerial spray deposition management.” 2002. Masters Thesis, University of Georgia. Accessed February 23, 2020. http://purl.galileo.usg.edu/uga_etd/wu_lei_200212_ms.

MLA Handbook (7^th Edition):

Wu, Lei. “A comparison of nature inspired intelligent optimization methods in aerial spray deposition management.” 2002. Web. 23 Feb 2020.

Vancouver:

Wu L. A comparison of nature inspired intelligent optimization methods in aerial spray deposition management. [Internet] [Masters thesis]. University of Georgia; 2002. [cited 2020 Feb 23]. Available from: http://purl.galileo.usg.edu/uga_etd/wu_lei_200212_ms.

Council of Science Editors:

Wu L. A comparison of nature inspired intelligent optimization methods in aerial spray deposition management. [Masters Thesis]. University of Georgia; 2002. Available from: http://purl.galileo.usg.edu/uga_etd/wu_lei_200212_ms

University of Georgia

7. Tariman, Kaan. Genetic algorithms for stochastic context-free grammar parameter estimation.

Degree: MS, Computer Science, 2004, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/tariman_kaan_200405_ms

► Stochastic grammar models for biological sequences have been extensively used in secondary structure prediction and profiling for structural homology recognition. A pertinent issue is, given… (more)

Subjects/Keywords: genetic algorithms

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APA (6^th Edition):

Tariman, K. (2004). Genetic algorithms for stochastic context-free grammar parameter estimation. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/tariman_kaan_200405_ms

Chicago Manual of Style (16^th Edition):

Tariman, Kaan. “Genetic algorithms for stochastic context-free grammar parameter estimation.” 2004. Masters Thesis, University of Georgia. Accessed February 23, 2020. http://purl.galileo.usg.edu/uga_etd/tariman_kaan_200405_ms.

MLA Handbook (7^th Edition):

Tariman, Kaan. “Genetic algorithms for stochastic context-free grammar parameter estimation.” 2004. Web. 23 Feb 2020.

Vancouver:

Tariman K. Genetic algorithms for stochastic context-free grammar parameter estimation. [Internet] [Masters thesis]. University of Georgia; 2004. [cited 2020 Feb 23]. Available from: http://purl.galileo.usg.edu/uga_etd/tariman_kaan_200405_ms.

Council of Science Editors:

Tariman K. Genetic algorithms for stochastic context-free grammar parameter estimation. [Masters Thesis]. University of Georgia; 2004. Available from: http://purl.galileo.usg.edu/uga_etd/tariman_kaan_200405_ms

University of Georgia

8. Chafekar, Deepti Ramesh. Multi-objective optimization using steady state genetic algorithms.

Degree: MS, Computer Science, 2004, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/chafekar_deepti_r_200412_ms

► There are many interesting problems in the real world that require multiple objectives to be satisfied at the same time. For many real world design… (more)

Subjects/Keywords: Genetic Algorithms

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APA (6^th Edition):

Chafekar, D. R. (2004). Multi-objective optimization using steady state genetic algorithms. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/chafekar_deepti_r_200412_ms

Chicago Manual of Style (16^th Edition):

Chafekar, Deepti Ramesh. “Multi-objective optimization using steady state genetic algorithms.” 2004. Masters Thesis, University of Georgia. Accessed February 23, 2020. http://purl.galileo.usg.edu/uga_etd/chafekar_deepti_r_200412_ms.

MLA Handbook (7^th Edition):

Chafekar, Deepti Ramesh. “Multi-objective optimization using steady state genetic algorithms.” 2004. Web. 23 Feb 2020.

Vancouver:

Chafekar DR. Multi-objective optimization using steady state genetic algorithms. [Internet] [Masters thesis]. University of Georgia; 2004. [cited 2020 Feb 23]. Available from: http://purl.galileo.usg.edu/uga_etd/chafekar_deepti_r_200412_ms.

Council of Science Editors:

Chafekar DR. Multi-objective optimization using steady state genetic algorithms. [Masters Thesis]. University of Georgia; 2004. Available from: http://purl.galileo.usg.edu/uga_etd/chafekar_deepti_r_200412_ms

University of Georgia

9. Zhang, Chongshan. Improving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness.

Degree: MS, Computer Science, 2005, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200505_ms

► In this thesis, we present two techniques to improve the performance of the genetic algorithm (GA). First we use Maximal Hyper-Rectangle (MHR) analysis to improve… (more)

Subjects/Keywords: Genetic Algorithms

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APA (6^th Edition):

Zhang, C. (2005). Improving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200505_ms

Chicago Manual of Style (16^th Edition):

Zhang, Chongshan. “Improving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness.” 2005. Masters Thesis, University of Georgia. Accessed February 23, 2020. http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200505_ms.

MLA Handbook (7^th Edition):

Zhang, Chongshan. “Improving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness.” 2005. Web. 23 Feb 2020.

Vancouver:

Zhang C. Improving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness. [Internet] [Masters thesis]. University of Georgia; 2005. [cited 2020 Feb 23]. Available from: http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200505_ms.

Council of Science Editors:

Zhang C. Improving GA performance by using Maximal Hyper-Rectangle analysis and relative fitness. [Masters Thesis]. University of Georgia; 2005. Available from: http://purl.galileo.usg.edu/uga_etd/zhang_chongshan_200505_ms

University of Georgia

10. Dewey, John Avery. Searching for prescriptive treatment schedules with a genetic algorithm: a tool for forest management.

Degree: MS, Artificial Intelligence, 2005, University of Georgia

URL: http://purl.galileo.usg.edu/uga_etd/dewey_john_a_200508_ms

► This thesis describes research on the use of a genetic algorithm (GA) to prescribe treatment plans for forest management at the stand level. Forest management… (more)

Subjects/Keywords: Genetic algorithms

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APA (6^th Edition):

Dewey, J. A. (2005). Searching for prescriptive treatment schedules with a genetic algorithm: a tool for forest management. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/dewey_john_a_200508_ms

Chicago Manual of Style (16^th Edition):

Dewey, John Avery. “Searching for prescriptive treatment schedules with a genetic algorithm: a tool for forest management.” 2005. Masters Thesis, University of Georgia. Accessed February 23, 2020. http://purl.galileo.usg.edu/uga_etd/dewey_john_a_200508_ms.

MLA Handbook (7^th Edition):

Dewey, John Avery. “Searching for prescriptive treatment schedules with a genetic algorithm: a tool for forest management.” 2005. Web. 23 Feb 2020.

Vancouver:

Dewey JA. Searching for prescriptive treatment schedules with a genetic algorithm: a tool for forest management. [Internet] [Masters thesis]. University of Georgia; 2005. [cited 2020 Feb 23]. Available from: http://purl.galileo.usg.edu/uga_etd/dewey_john_a_200508_ms.

Council of Science Editors:

Dewey JA. Searching for prescriptive treatment schedules with a genetic algorithm: a tool for forest management. [Masters Thesis]. University of Georgia; 2005. Available from: http://purl.galileo.usg.edu/uga_etd/dewey_john_a_200508_ms

University of Alberta

11. Shu, Lingyan. The impact of data structures on the performance of genetic-algorithm-based learning.

Degree: PhD, Department of Computing Science, 1992, University of Alberta

URL: https://era.library.ualberta.ca/files/bg257h66m

Subjects/Keywords: Genetic algorithms.

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APA (6^th Edition):

Shu, L. (1992). The impact of data structures on the performance of genetic-algorithm-based learning. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/bg257h66m

Chicago Manual of Style (16^th Edition):

Shu, Lingyan. “The impact of data structures on the performance of genetic-algorithm-based learning.” 1992. Doctoral Dissertation, University of Alberta. Accessed February 23, 2020. https://era.library.ualberta.ca/files/bg257h66m.

MLA Handbook (7^th Edition):

Shu, Lingyan. “The impact of data structures on the performance of genetic-algorithm-based learning.” 1992. Web. 23 Feb 2020.

Vancouver:

Shu L. The impact of data structures on the performance of genetic-algorithm-based learning. [Internet] [Doctoral dissertation]. University of Alberta; 1992. [cited 2020 Feb 23]. Available from: https://era.library.ualberta.ca/files/bg257h66m.

Council of Science Editors:

Shu L. The impact of data structures on the performance of genetic-algorithm-based learning. [Doctoral Dissertation]. University of Alberta; 1992. Available from: https://era.library.ualberta.ca/files/bg257h66m

Texas A&M University

12. Gibbs, Trevor Howard. Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms.

Degree: 2011, Texas A&M University

URL: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399

► Horizontal well placement determination within a reservoir is a significant and difficult step in the reservoir development process. Determining the optimal well location is a… (more)

▼ Horizontal well placement determination within a reservoir is a significant and difficult step in the reservoir development process. Determining the optimal well location is a complex problem involving many factors including geological considerations, reservoir and fluid properties, economic costs, lateral direction, and technical ability. The most thorough approach to this problem is that of an exhaustive search, in which a simulation is run for every conceivable well position in the reservoir. Although thorough and accurate, this approach is typically not used in real world applications due to the time constraints from the excessive number of simulations. This project suggests the use of a genetic algorithm applied to the horizontal well placement problem in a gas reservoir to reduce the required number of simulations. This research aims to first determine if well placement optimization is even necessary in a gas reservoir, and if so, to determine the benefit of optimization. Performance of the genetic algorithm was analyzed through five different case scenarios, one involving a vertical well and four involving horizontal wells. The genetic algorithm approach is used to evaluate the effect of well placement in heterogeneous and anisotropic reservoirs on reservoir recovery. The wells are constrained by surface gas rate and bottom-hole pressure for each case. This project's main new contribution is its application of using genetic algorithms to study the effect of well placement optimization in gas reservoirs. Two fundamental questions have been answered in this research. First, does well placement in a gas reservoir affect the reservoir performance? If so, what is an efficient method to find the optimal well location based on reservoir performance? The research provides evidence that well placement optimization is an important criterion during the reservoir development phase of a horizontal-well project in gas reservoirs, but it is less significant to vertical wells in a homogeneous reservoir. It is also shown that genetic algorithms are an extremely efficient and robust tool to find the optimal location. Advisors/Committee Members: Zhu, Ding (advisor), Nasrabadi, Hadi (advisor), Sun, Yuefeng (committee member), Ehlig-Economides, Christine (committee member).

Subjects/Keywords: genetic; algorithms; optimization; well placement

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APA (6^th Edition):

Gibbs, T. H. (2011). Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399

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):

Gibbs, Trevor Howard. “Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms.” 2011. Thesis, Texas A&M University. Accessed February 23, 2020. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399.

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

MLA Handbook (7^th Edition):

Gibbs, Trevor Howard. “Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms.” 2011. Web. 23 Feb 2020.

Vancouver:

Gibbs TH. Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms. [Internet] [Thesis]. Texas A&M University; 2011. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399.

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

Council of Science Editors:

Gibbs TH. Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms. [Thesis]. Texas A&M University; 2011. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399

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

University of Hong Kong

13. Liu, Xinyi. A general RNA secondary structure algorithm with vertical tree grammar.

Degree: M. Phil., 2013, University of Hong Kong

URL: Liu, X. [刘欣怡]. (2013). A general RNA secondary structure algorithm with vertical tree grammar. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177352 ; http://dx.doi.org/10.5353/th_b5177352 ; http://hdl.handle.net/10722/196439

►

Our understanding of the functions played by RNA molecules is expanded with the understanding of RNA structures. Except for primary structure, RNA molecules present pairings… (more)

Subjects/Keywords: RNA - Structure; Genetic algorithms

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APA (6^th Edition):

Liu, X. (2013). A general RNA secondary structure algorithm with vertical tree grammar. (Masters Thesis). University of Hong Kong. Retrieved from Liu, X. [刘欣怡]. (2013). A general RNA secondary structure algorithm with vertical tree grammar. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177352 ; http://dx.doi.org/10.5353/th_b5177352 ; http://hdl.handle.net/10722/196439

Chicago Manual of Style (16^th Edition):

Liu, Xinyi. “A general RNA secondary structure algorithm with vertical tree grammar.” 2013. Masters Thesis, University of Hong Kong. Accessed February 23, 2020. Liu, X. [刘欣怡]. (2013). A general RNA secondary structure algorithm with vertical tree grammar. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177352 ; http://dx.doi.org/10.5353/th_b5177352 ; http://hdl.handle.net/10722/196439.

MLA Handbook (7^th Edition):

Liu, Xinyi. “A general RNA secondary structure algorithm with vertical tree grammar.” 2013. Web. 23 Feb 2020.

Vancouver:

Liu X. A general RNA secondary structure algorithm with vertical tree grammar. [Internet] [Masters thesis]. University of Hong Kong; 2013. [cited 2020 Feb 23]. Available from: Liu, X. [刘欣怡]. (2013). A general RNA secondary structure algorithm with vertical tree grammar. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177352 ; http://dx.doi.org/10.5353/th_b5177352 ; http://hdl.handle.net/10722/196439.

Council of Science Editors:

Liu X. A general RNA secondary structure algorithm with vertical tree grammar. [Masters Thesis]. University of Hong Kong; 2013. Available from: Liu, X. [刘欣怡]. (2013). A general RNA secondary structure algorithm with vertical tree grammar. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177352 ; http://dx.doi.org/10.5353/th_b5177352 ; http://hdl.handle.net/10722/196439

University of Hong Kong

14. Li, Ping. Analysis and synthesis of positive systems and related gene network models.

Degree: PhD, 2011, University of Hong Kong

URL: Li, P. [李平]. (2011). Analysis and synthesis of positive systems and related gene network models. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4658702 ; http://dx.doi.org/10.5353/th_b4658702 ; http://hdl.handle.net/10722/143209

The Best PhD Thesis in the Faculties of Dentistry, Engineering, Medicine and Science (University of Hong Kong), Li Ka Shing Prize,2010-11

published_or_final_version

Mechanical Engineering

Doctoral

Doctor of Philosophy

Advisors/Committee Members: Lam, J.

Subjects/Keywords: Genetic algorithms.; Positive systems.

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APA (6^th Edition):

Li, P. (2011). Analysis and synthesis of positive systems and related gene network models. (Doctoral Dissertation). University of Hong Kong. Retrieved from Li, P. [李平]. (2011). Analysis and synthesis of positive systems and related gene network models. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4658702 ; http://dx.doi.org/10.5353/th_b4658702 ; http://hdl.handle.net/10722/143209

Chicago Manual of Style (16^th Edition):

Li, Ping. “Analysis and synthesis of positive systems and related gene network models.” 2011. Doctoral Dissertation, University of Hong Kong. Accessed February 23, 2020. Li, P. [李平]. (2011). Analysis and synthesis of positive systems and related gene network models. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4658702 ; http://dx.doi.org/10.5353/th_b4658702 ; http://hdl.handle.net/10722/143209.

MLA Handbook (7^th Edition):

Li, Ping. “Analysis and synthesis of positive systems and related gene network models.” 2011. Web. 23 Feb 2020.

Vancouver:

Li P. Analysis and synthesis of positive systems and related gene network models. [Internet] [Doctoral dissertation]. University of Hong Kong; 2011. [cited 2020 Feb 23]. Available from: Li, P. [李平]. (2011). Analysis and synthesis of positive systems and related gene network models. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4658702 ; http://dx.doi.org/10.5353/th_b4658702 ; http://hdl.handle.net/10722/143209.

Council of Science Editors:

Li P. Analysis and synthesis of positive systems and related gene network models. [Doctoral Dissertation]. University of Hong Kong; 2011. Available from: Li, P. [李平]. (2011). Analysis and synthesis of positive systems and related gene network models. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b4658702 ; http://dx.doi.org/10.5353/th_b4658702 ; http://hdl.handle.net/10722/143209

Rochester Institute of Technology

15. Lamos-Sweeney, Joshua. Deep learning using genetic algorithms.

Degree: Computer Science (GCCIS), 2012, Rochester Institute of Technology

URL: https://scholarworks.rit.edu/theses/254

► Deep Learning networks are a new type of neural network that discovers important object features. These networks determine features without supervision, and are adept at… (more)

Subjects/Keywords: Deep learning; Genetic algorithms

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APA (6^th Edition):

Lamos-Sweeney, J. (2012). Deep learning using genetic algorithms. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/254

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):

Lamos-Sweeney, Joshua. “Deep learning using genetic algorithms.” 2012. Thesis, Rochester Institute of Technology. Accessed February 23, 2020. https://scholarworks.rit.edu/theses/254.

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

MLA Handbook (7^th Edition):

Lamos-Sweeney, Joshua. “Deep learning using genetic algorithms.” 2012. Web. 23 Feb 2020.

Vancouver:

Lamos-Sweeney J. Deep learning using genetic algorithms. [Internet] [Thesis]. Rochester Institute of Technology; 2012. [cited 2020 Feb 23]. Available from: https://scholarworks.rit.edu/theses/254.

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

Council of Science Editors:

Lamos-Sweeney J. Deep learning using genetic algorithms. [Thesis]. Rochester Institute of Technology; 2012. Available from: https://scholarworks.rit.edu/theses/254

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

Montana State University

16. Hayes, Christina Savannah Maria. Generic properties of the infinite population genetic algorithm.

Degree: College of Letters & Science, 2006, Montana State University

URL: https://scholarworks.montana.edu/xmlui/handle/1/1448

► The infinite population model for the genetic algorithm, where the iteration of the genetic algorithm corresponds to an iteration of a map G, is a… (more)

Subjects/Keywords: Genetic algorithms.

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APA (6^th Edition):

Hayes, C. S. M. (2006). Generic properties of the infinite population genetic algorithm. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/1448

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):

Hayes, Christina Savannah Maria. “Generic properties of the infinite population genetic algorithm.” 2006. Thesis, Montana State University. Accessed February 23, 2020. https://scholarworks.montana.edu/xmlui/handle/1/1448.

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

MLA Handbook (7^th Edition):

Hayes, Christina Savannah Maria. “Generic properties of the infinite population genetic algorithm.” 2006. Web. 23 Feb 2020.

Vancouver:

Hayes CSM. Generic properties of the infinite population genetic algorithm. [Internet] [Thesis]. Montana State University; 2006. [cited 2020 Feb 23]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/1448.

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

Council of Science Editors:

Hayes CSM. Generic properties of the infinite population genetic algorithm. [Thesis]. Montana State University; 2006. Available from: https://scholarworks.montana.edu/xmlui/handle/1/1448

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

Texas A&M University

17. Kumar, Akansha. An Iterative Optimization Method Using Genetic Algorithms and Gaussian Process Based Regression in Nuclear Reactor Design Applications.

Degree: PhD, Nuclear Engineering, 2016, Texas A&M University

URL: http://hdl.handle.net/1969.1/159121

► The optimization of a complex system involves the determination of optimum values for a set of design parameters. The optimization search happens in order to… (more)

Subjects/Keywords: genetic algorithms; optimization; nuclear; reactor

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APA (6^th Edition):

Kumar, A. (2016). An Iterative Optimization Method Using Genetic Algorithms and Gaussian Process Based Regression in Nuclear Reactor Design Applications. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/159121

Chicago Manual of Style (16^th Edition):

Kumar, Akansha. “An Iterative Optimization Method Using Genetic Algorithms and Gaussian Process Based Regression in Nuclear Reactor Design Applications.” 2016. Doctoral Dissertation, Texas A&M University. Accessed February 23, 2020. http://hdl.handle.net/1969.1/159121.

MLA Handbook (7^th Edition):

Kumar, Akansha. “An Iterative Optimization Method Using Genetic Algorithms and Gaussian Process Based Regression in Nuclear Reactor Design Applications.” 2016. Web. 23 Feb 2020.

Vancouver:

Kumar A. An Iterative Optimization Method Using Genetic Algorithms and Gaussian Process Based Regression in Nuclear Reactor Design Applications. [Internet] [Doctoral dissertation]. Texas A&M University; 2016. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/1969.1/159121.

Council of Science Editors:

Kumar A. An Iterative Optimization Method Using Genetic Algorithms and Gaussian Process Based Regression in Nuclear Reactor Design Applications. [Doctoral Dissertation]. Texas A&M University; 2016. Available from: http://hdl.handle.net/1969.1/159121

Montana State University

18. Pamplin, Trenton L. Hybridizing statistics with genetic algorithms.

Degree: College of Letters & Science, 1995, Montana State University

URL: https://scholarworks.montana.edu/xmlui/handle/1/7586

Subjects/Keywords: Genetic algorithms.

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APA (6^th Edition):

Pamplin, T. L. (1995). Hybridizing statistics with genetic algorithms. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/7586

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):

Pamplin, Trenton L. “Hybridizing statistics with genetic algorithms.” 1995. Thesis, Montana State University. Accessed February 23, 2020. https://scholarworks.montana.edu/xmlui/handle/1/7586.

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

MLA Handbook (7^th Edition):

Pamplin, Trenton L. “Hybridizing statistics with genetic algorithms.” 1995. Web. 23 Feb 2020.

Vancouver:

Pamplin TL. Hybridizing statistics with genetic algorithms. [Internet] [Thesis]. Montana State University; 1995. [cited 2020 Feb 23]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/7586.

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

Council of Science Editors:

Pamplin TL. Hybridizing statistics with genetic algorithms. [Thesis]. Montana State University; 1995. Available from: https://scholarworks.montana.edu/xmlui/handle/1/7586

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

Oregon State University

19. Dill, Karen M. Growing digital circuits : logic synthesis and minimization with genetic operators.

Degree: MS, Electrical and Computer Engineering, 1996, Oregon State University

URL: http://hdl.handle.net/1957/34241

► This research applies the biologically inspired, artificial evolutionary processes of Genetic Algorithms and Genetic Programming to digital hardware circuit synthesis and minimization. In this new… (more)

▼ This research applies the biologically inspired, artificial evolutionary processes of Genetic Algorithms and Genetic Programming to digital hardware circuit synthesis and minimization. In this new application, three approaches are taken to genetic hardware development. First, as a method for logic synthesis, Genetic Programming is applied to the building of logic functions. Experimental results have shown the logic equations from this technique produce better than 88% coverage of the given truth-tables, but the method cannot guarantee complete (100%) coverage. Secondly, to better achieve complete function coverage, an XOR Correction Circuit Algorithm used in conjunction with the Genetic Logic Synthesis was developed. With this algorithm, the genetic logic synthesis can reiteratively attempt coverage by formulating its own selective "correction" functions, for input combinations where complete truth table coverage has not previously been achieved. With this technique, complete function coverage was synthesized in all experiments conducted. The third application of the paradigm is to the minimization of Reed-Muller Equations. In this application, a Genetic Algorithm is implemented only in the search space of all "correct", functionally equivalent equations, with only the task of finding reductions. With this limited search space the solutions have absolute guaranteed function coverage, as well as a better defined focus for the genetic evolutionary process. In both the logic synthesis and minimization processes the genetic operators determine efficient circuit implementations and reductions. The results are often different from those of human designers. Because the genetic techniques incorporate logical testing into the design and build process, one can be assured that the circuit will function as derived on completion. For all three applications, the effects of a number of evolutionary parameters on the genetic operators' problem solving capability are examined. The resulting logic and logic minimizations are also compared with both arbitrarily defined functions and well known logic synthesis benchmarks. It has been shown that genetic operators applied to digital logic can effectively find good solutions for both logic synthesis and logic minimization. Advisors/Committee Members: Herzog, James H. (advisor).

Subjects/Keywords: Genetic algorithms

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APA (6^th Edition):

Dill, K. M. (1996). Growing digital circuits : logic synthesis and minimization with genetic operators. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/34241

Chicago Manual of Style (16^th Edition):

Dill, Karen M. “Growing digital circuits : logic synthesis and minimization with genetic operators.” 1996. Masters Thesis, Oregon State University. Accessed February 23, 2020. http://hdl.handle.net/1957/34241.

MLA Handbook (7^th Edition):

Dill, Karen M. “Growing digital circuits : logic synthesis and minimization with genetic operators.” 1996. Web. 23 Feb 2020.

Vancouver:

Dill KM. Growing digital circuits : logic synthesis and minimization with genetic operators. [Internet] [Masters thesis]. Oregon State University; 1996. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/1957/34241.

Council of Science Editors:

Dill KM. Growing digital circuits : logic synthesis and minimization with genetic operators. [Masters Thesis]. Oregon State University; 1996. Available from: http://hdl.handle.net/1957/34241

Oregon State University

20. Canpolat, Necati. Optimization of seasonal irrigation scheduling by genetic algorithms.

Degree: PhD, Bioresource Engineering, 1997, Oregon State University

URL: http://hdl.handle.net/1957/34323

► In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs… (more)

▼ In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of optimizing nonlinear crop models and describe application requirements and implementation of the technique. GAs were found to converge quickly to near-optimal solutions. Second, we analyze the relationship between GA control parameters (population size, crossover rate, and mutation rate) and performance. We identify a combination of population, mutation, and crossover which searched the fitness landscape efficiently. The results suggest that smaller populations are able to provide better performance at relatively low mutation rates. More stable outcomes were generated using low mutation rates. Without crossover the quality of solutions were generally impaired, and the search process was lengthened. Aside from crossover rate zero, no other crossover rates significantly differed. The behaviors observed for best, online, offline, and average performances were sensitive to the combined influences control parameters. Interaction among control parameters was strongly indicated. Finally, several adaptive penalty techniques are presented for handling constraints in GAs, and their effectiveness is demonstrated. The constant penalty function suffered from sensitivity to settings of penalty coefficients, and was not successful in satisfying constraints. The adaptive penalty functions utilizes violation distance based metrics and search time based scaling using generation or trials number, and fitness values to penalize infeasible solutions, as the distance from the feasible region or number of generations increases so does the penalty. They were quite successful in providing solutions with minimal effort. They adapt the penalty as the search continues, encouraging feasible solutions to emerge over the time. Adaptive approaches presented here are flexible, efficient, and robust to parameter settings. Advisors/Committee Members: Bolte, John P. (advisor), Moss, Dave (committee member).

Subjects/Keywords: Genetic algorithms

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APA (6^th Edition):

Canpolat, N. (1997). Optimization of seasonal irrigation scheduling by genetic algorithms. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34323

Chicago Manual of Style (16^th Edition):

Canpolat, Necati. “Optimization of seasonal irrigation scheduling by genetic algorithms.” 1997. Doctoral Dissertation, Oregon State University. Accessed February 23, 2020. http://hdl.handle.net/1957/34323.

MLA Handbook (7^th Edition):

Canpolat, Necati. “Optimization of seasonal irrigation scheduling by genetic algorithms.” 1997. Web. 23 Feb 2020.

Vancouver:

Canpolat N. Optimization of seasonal irrigation scheduling by genetic algorithms. [Internet] [Doctoral dissertation]. Oregon State University; 1997. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/1957/34323.

Council of Science Editors:

Canpolat N. Optimization of seasonal irrigation scheduling by genetic algorithms. [Doctoral Dissertation]. Oregon State University; 1997. Available from: http://hdl.handle.net/1957/34323

University of Oklahoma

21. Carlson, Benjamin. Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms.

Degree: PhD, 2016, University of Oklahoma

URL: http://hdl.handle.net/11244/34654

► Many approaches to applying Genetic Algorithms (GAs) to Nondeterministic Polynomial time Complete (NPC) problems involve population members encoded directly from the problem solution space. While… (more)

▼ Many approaches to applying Genetic Algorithms (GAs) to Nondeterministic Polynomial time Complete (NPC) problems involve population members encoded directly from the problem solution space. While this technique enables trivial mapping of the population members to solutions, it can cause complex problems for GA operators as they attempt to direct the evolution of the population toward more promising areas of the solution space. These operators, using inspiration from genetics and evolution in the biological world, combine and manipulate the current population to produce a new population that, it is hoped, will eventually converge toward better solutions to the original problem. However, many problems, especially graph-space problems, cannot be so easily manipulated when GA members consist of direct encodings. In such cases, GA operators must perform awkward transformations to convert the progeny into viable solutions. Here is where heuristic encoding comes into play, in that any combination of genes will produce a viable solution. However, this additional level of abstraction does cause other problems and tends to weaken the guiding effects of traditional GA operators. Thus, I have designed custom GA operators that mitigate these problems by using the solutions produced by the heuristic encoded members to better guide the manipulation when producing the next generation. This dissertation shows that heuristic encoding is an effective technique for the representation of solutions to graph-space problems. It also shows that, when using heuristic encoding, GAs with traditional operators perform well compared to more direct encoding techniques. Finally it shows the combination of heuristic encoding and GA operators designed to work with them increases GA performance and can be competitive with other techniques. I believe that these techniques will also work well for other types of problems for which GAs are commonly applied. Advisors/Committee Members: Hougen, Dean (advisor), Havlicek, Joseph (committee member), Atiquzzaman, Mohammed (committee member), Cheng, Samuel (committee member), Radhakrishnan, Sridhar (committee member).

Subjects/Keywords: Heuristics; Genetic Algorithms; Evolutionary Computation

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APA (6^th Edition):

Carlson, B. (2016). Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/34654

Chicago Manual of Style (16^th Edition):

Carlson, Benjamin. “Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms.” 2016. Doctoral Dissertation, University of Oklahoma. Accessed February 23, 2020. http://hdl.handle.net/11244/34654.

MLA Handbook (7^th Edition):

Carlson, Benjamin. “Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms.” 2016. Web. 23 Feb 2020.

Vancouver:

Carlson B. Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms. [Internet] [Doctoral dissertation]. University of Oklahoma; 2016. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/11244/34654.

Council of Science Editors:

Carlson B. Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms. [Doctoral Dissertation]. University of Oklahoma; 2016. Available from: http://hdl.handle.net/11244/34654

Nelson Mandela Metropolitan University

22. Buys, Stefan. Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition.

Degree: Faculty of Engineering, the Built Environment and Information Technology, 2012, Nelson Mandela Metropolitan University

URL: http://hdl.handle.net/10948/d1008356

► Object or part recognition is of major interest in industrial environments. Current methods implement expensive camera based solutions. There is a need for a cost… (more)

Subjects/Keywords: Genetic algorithms; Software architecture

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APA (6^th Edition):

Buys, S. (2012). Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition. (Thesis). Nelson Mandela Metropolitan University. Retrieved from http://hdl.handle.net/10948/d1008356

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):

Buys, Stefan. “Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition.” 2012. Thesis, Nelson Mandela Metropolitan University. Accessed February 23, 2020. http://hdl.handle.net/10948/d1008356.

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

MLA Handbook (7^th Edition):

Buys, Stefan. “Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition.” 2012. Web. 23 Feb 2020.

Vancouver:

Buys S. Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition. [Internet] [Thesis]. Nelson Mandela Metropolitan University; 2012. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/10948/d1008356.

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

Council of Science Editors:

Buys S. Genetic algorithm for Artificial Neural Network training for the purpose of Automated Part Recognition. [Thesis]. Nelson Mandela Metropolitan University; 2012. Available from: http://hdl.handle.net/10948/d1008356

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

University of New South Wales

23. Chi, Tzi-Chieh. Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles.

Degree: Mechanical & Manufacturing Engineering, 2015, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/54276 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34575/SOURCE02?view=true

► Evolving swarms have advantages over monolithic vehicles in a number of situations. They are able to distribute their sensors more widely, require less sophistication and… (more)

▼ Evolving swarms have advantages over monolithic vehicles in a number of situations. They are able to distribute their sensors more widely, require less sophistication and are robust in that the failure of one element leads to the degradation of the swarm rather than complete failure. Significant benefits of these characteristics include greater endurance and the ability to operate in dangerous environments.Two applications were developed in this thesis to investigate the technological feasibility of an ever evolving swarm in different technological areas. While one part, of the research, explored the ruggedness bestowed on a space system in a debris field by using distributed agents the other looked at how their performance could be improved in a much simpler scenario by using evolutionary algorithms. The aims of this thesis are to explore the feasibility of using an evolutionary method to develop the rules governing self-organised systems to coordinate and control swarms of satellites in debris avoidance manoeuvres and UAVs in marine search and rescue (SAR) missions. The proposed models are capable of improving the energy efficiency of two different methods by which a cluster of spacecraft can perform a scatter and re-gather manoeuvre to rapidly avoid collision with space debris; as well as allowing SAR co-ordinators to better manage their man-power and resources for an increased chance of target identification within a desired timeframe.A swarm intelligent model was developed and implemented using the agent-based software, NetLogo© for SAR missions. The model used self-organised emergent behaviour with an evolutionary algorithm to optimise the effectiveness of the swarm in adapting to changing environments. The spacecraft debris avoidance case study model was developed in Matlab and validated by comparing it with a STK / Astrogator generated ephemeris 3D model within an orbital environment simulator. The elliptic formation validated through STK was able to achieve the same performance in Matlab in a continuous and nonlinear model through incorporating J2 perturbations.The results show a swarm-intelligence based approach is effective, robust and scalable. Both of the developed scattering manoeuvres were able to maintain the desired cluster flight formation in J2 perturbations with Δv optimised to minimise fuel consumption. In addition, the SAR UAVs demonstrated the ability to form teams, fly in formation and find the targets with only initial human input required being to generate a fitness factor; with results showing that there is an effective division of labour. Advisors/Committee Members: Page, John, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW, Olsen, John, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW, Tsafnat, Naomi, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW.

Subjects/Keywords: Genetic Algorithms; Swarm; Self-Organising

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

APA (6^th Edition):

Chi, T. (2015). Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles. (Masters Thesis). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/54276 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34575/SOURCE02?view=true

Chicago Manual of Style (16^th Edition):

Chi, Tzi-Chieh. “Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles.” 2015. Masters Thesis, University of New South Wales. Accessed February 23, 2020. http://handle.unsw.edu.au/1959.4/54276 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34575/SOURCE02?view=true.

MLA Handbook (7^th Edition):

Chi, Tzi-Chieh. “Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles.” 2015. Web. 23 Feb 2020.

Vancouver:

Chi T. Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles. [Internet] [Masters thesis]. University of New South Wales; 2015. [cited 2020 Feb 23]. Available from: http://handle.unsw.edu.au/1959.4/54276 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34575/SOURCE02?view=true.

Council of Science Editors:

Chi T. Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles. [Masters Thesis]. University of New South Wales; 2015. Available from: http://handle.unsw.edu.au/1959.4/54276 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:34575/SOURCE02?view=true

24. Henshall, Gareth. Optimising kinematic systems using crowd-sourcing and genetic algorithms.

Degree: PhD, 2019, Bangor University

URL: https://research.bangor.ac.uk/portal/en/theses/optimising-kinematic-systems-using-crowdsourcing-and-genetic-algorithms(788570df-afc1-49d6-abad-965cf8370d6d).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782099

► Procedural animation systems are capable of synthesising life-like organic motion automatically. However due to extensive parameterisation, tuning these systems can be very difficult. Not only… (more)

Subjects/Keywords: optimisation; crowd-sourcing; genetic algorithms

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

APA (6^th Edition):

Henshall, G. (2019). Optimising kinematic systems using crowd-sourcing and genetic algorithms. (Doctoral Dissertation). Bangor University. Retrieved from https://research.bangor.ac.uk/portal/en/theses/optimising-kinematic-systems-using-crowdsourcing-and-genetic-algorithms(788570df-afc1-49d6-abad-965cf8370d6d).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782099

Chicago Manual of Style (16^th Edition):

Henshall, Gareth. “Optimising kinematic systems using crowd-sourcing and genetic algorithms.” 2019. Doctoral Dissertation, Bangor University. Accessed February 23, 2020. https://research.bangor.ac.uk/portal/en/theses/optimising-kinematic-systems-using-crowdsourcing-and-genetic-algorithms(788570df-afc1-49d6-abad-965cf8370d6d).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782099.

MLA Handbook (7^th Edition):

Henshall, Gareth. “Optimising kinematic systems using crowd-sourcing and genetic algorithms.” 2019. Web. 23 Feb 2020.

Vancouver:

Henshall G. Optimising kinematic systems using crowd-sourcing and genetic algorithms. [Internet] [Doctoral dissertation]. Bangor University; 2019. [cited 2020 Feb 23]. Available from: https://research.bangor.ac.uk/portal/en/theses/optimising-kinematic-systems-using-crowdsourcing-and-genetic-algorithms(788570df-afc1-49d6-abad-965cf8370d6d).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782099.

Council of Science Editors:

Henshall G. Optimising kinematic systems using crowd-sourcing and genetic algorithms. [Doctoral Dissertation]. Bangor University; 2019. Available from: https://research.bangor.ac.uk/portal/en/theses/optimising-kinematic-systems-using-crowdsourcing-and-genetic-algorithms(788570df-afc1-49d6-abad-965cf8370d6d).html ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.782099

University of Guelph

25. Brown, Joseph Alexander. Regression and Classification from Extinction .

Degree: 2014, University of Guelph

URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793

► Evolutionary Algorithms use the principles of natural selection and biological evolution to act as search and optimization tools. Two novel Spatially Structured Evolutionary Algorithms: the… (more)

Subjects/Keywords: Genetic Algorithms; Bioinformatics; Evolutionary Computation

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

APA (6^th Edition):

Brown, J. A. (2014). Regression and Classification from Extinction . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793

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):

Brown, Joseph Alexander. “Regression and Classification from Extinction .” 2014. Thesis, University of Guelph. Accessed February 23, 2020. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793.

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

MLA Handbook (7^th Edition):

Brown, Joseph Alexander. “Regression and Classification from Extinction .” 2014. Web. 23 Feb 2020.

Vancouver:

Brown JA. Regression and Classification from Extinction . [Internet] [Thesis]. University of Guelph; 2014. [cited 2020 Feb 23]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793.

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

Council of Science Editors:

Brown JA. Regression and Classification from Extinction . [Thesis]. University of Guelph; 2014. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793

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

University of Adelaide

26. Sullivan, Nicholas David. Task Allocation and Collaborative Localisation in Multi-Robot Systems.

Degree: 2019, University of Adelaide

URL: http://hdl.handle.net/2440/120578

► To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation.… (more)

▼ To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis. Advisors/Committee Members: Grainger, Steven (advisor), School of Mechanical Engineering (school).

Subjects/Keywords: multi-robot; genetic algorithms; localisation

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APA (6^th Edition):

Sullivan, N. D. (2019). Task Allocation and Collaborative Localisation in Multi-Robot Systems. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/120578

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):

Sullivan, Nicholas David. “Task Allocation and Collaborative Localisation in Multi-Robot Systems.” 2019. Thesis, University of Adelaide. Accessed February 23, 2020. http://hdl.handle.net/2440/120578.

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

MLA Handbook (7^th Edition):

Sullivan, Nicholas David. “Task Allocation and Collaborative Localisation in Multi-Robot Systems.” 2019. Web. 23 Feb 2020.

Vancouver:

Sullivan ND. Task Allocation and Collaborative Localisation in Multi-Robot Systems. [Internet] [Thesis]. University of Adelaide; 2019. [cited 2020 Feb 23]. Available from: http://hdl.handle.net/2440/120578.

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

Council of Science Editors:

Sullivan ND. Task Allocation and Collaborative Localisation in Multi-Robot Systems. [Thesis]. University of Adelaide; 2019. Available from: http://hdl.handle.net/2440/120578

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

Hong Kong University of Science and Technology

27. Law, Nga Lam. Parameter-free adaptive genetic algorithm.

Degree: 2007, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-5471 ; https://doi.org/10.14711/thesis-b987302 ; http://repository.ust.hk/ir/bitstream/1783.1-5471/1/th_redirect.html

► A new adaptive approach, which applies the concept of relative importance of information to the genetic algorithm, is developed. Selection, crossover and mutation are parameter-free… (more)

Subjects/Keywords: Genetic algorithms

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):

Law, N. L. (2007). Parameter-free adaptive genetic algorithm. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-5471 ; https://doi.org/10.14711/thesis-b987302 ; http://repository.ust.hk/ir/bitstream/1783.1-5471/1/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):

Law, Nga Lam. “Parameter-free adaptive genetic algorithm.” 2007. Thesis, Hong Kong University of Science and Technology. Accessed February 23, 2020. http://repository.ust.hk/ir/Record/1783.1-5471 ; https://doi.org/10.14711/thesis-b987302 ; http://repository.ust.hk/ir/bitstream/1783.1-5471/1/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):

Law, Nga Lam. “Parameter-free adaptive genetic algorithm.” 2007. Web. 23 Feb 2020.

Vancouver:

Law NL. Parameter-free adaptive genetic algorithm. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2007. [cited 2020 Feb 23]. Available from: http://repository.ust.hk/ir/Record/1783.1-5471 ; https://doi.org/10.14711/thesis-b987302 ; http://repository.ust.hk/ir/bitstream/1783.1-5471/1/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:

Law NL. Parameter-free adaptive genetic algorithm. [Thesis]. Hong Kong University of Science and Technology; 2007. Available from: http://repository.ust.hk/ir/Record/1783.1-5471 ; https://doi.org/10.14711/thesis-b987302 ; http://repository.ust.hk/ir/bitstream/1783.1-5471/1/th_redirect.html

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

Michigan State University

28. Buyukbozkirli, Bulent. Modeling genetic algorithm dynamics for OneMax and deceptive functions.

Degree: PhD, Department of Mathematics, 2004, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:32812

Subjects/Keywords: Genetic algorithms

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

APA (6^th Edition):

Buyukbozkirli, B. (2004). Modeling genetic algorithm dynamics for OneMax and deceptive functions. (Doctoral Dissertation). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:32812

Chicago Manual of Style (16^th Edition):

Buyukbozkirli, Bulent. “Modeling genetic algorithm dynamics for OneMax and deceptive functions.” 2004. Doctoral Dissertation, Michigan State University. Accessed February 23, 2020. http://etd.lib.msu.edu/islandora/object/etd:32812.

MLA Handbook (7^th Edition):

Buyukbozkirli, Bulent. “Modeling genetic algorithm dynamics for OneMax and deceptive functions.” 2004. Web. 23 Feb 2020.

Vancouver:

Buyukbozkirli B. Modeling genetic algorithm dynamics for OneMax and deceptive functions. [Internet] [Doctoral dissertation]. Michigan State University; 2004. [cited 2020 Feb 23]. Available from: http://etd.lib.msu.edu/islandora/object/etd:32812.

Council of Science Editors:

Buyukbozkirli B. Modeling genetic algorithm dynamics for OneMax and deceptive functions. [Doctoral Dissertation]. Michigan State University; 2004. Available from: http://etd.lib.msu.edu/islandora/object/etd:32812

Michigan State University

29. Pei, Huafeng. A comparison of cohort GA with canonical serial and island-model distributed GA's.

Degree: MS, Department of Electrical and Computer Engineering, 2000, Michigan State University

URL: http://etd.lib.msu.edu/islandora/object/etd:30589

Subjects/Keywords: Genetic algorithms

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):

Pei, H. (2000). A comparison of cohort GA with canonical serial and island-model distributed GA's. (Masters Thesis). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:30589

Chicago Manual of Style (16^th Edition):

Pei, Huafeng. “A comparison of cohort GA with canonical serial and island-model distributed GA's.” 2000. Masters Thesis, Michigan State University. Accessed February 23, 2020. http://etd.lib.msu.edu/islandora/object/etd:30589.

MLA Handbook (7^th Edition):

Pei, Huafeng. “A comparison of cohort GA with canonical serial and island-model distributed GA's.” 2000. Web. 23 Feb 2020.

Vancouver:

Pei H. A comparison of cohort GA with canonical serial and island-model distributed GA's. [Internet] [Masters thesis]. Michigan State University; 2000. [cited 2020 Feb 23]. Available from: http://etd.lib.msu.edu/islandora/object/etd:30589.

Council of Science Editors:

Pei H. A comparison of cohort GA with canonical serial and island-model distributed GA's. [Masters Thesis]. Michigan State University; 2000. Available from: http://etd.lib.msu.edu/islandora/object/etd:30589

Chapman University

30. James, Kevin. Estimating Auction Equilibria using Individual Evolutionary Learning.

Degree: PhD, Computational and Data Sciences, 2019, Chapman University

URL: https://digitalcommons.chapman.edu/cads_dissertations/1

► I develop the Generalized Evolutionary Nash Equilibrium Estimator (GENEE) library. The tool is designed to provide a generic computational library for running genetic algorithms… (more)

Subjects/Keywords: genetic algorithms; Behavioral Economics

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

APA (6^th Edition):

James, K. (2019). Estimating Auction Equilibria using Individual Evolutionary Learning. (Doctoral Dissertation). Chapman University. Retrieved from https://digitalcommons.chapman.edu/cads_dissertations/1

Chicago Manual of Style (16^th Edition):

James, Kevin. “Estimating Auction Equilibria using Individual Evolutionary Learning.” 2019. Doctoral Dissertation, Chapman University. Accessed February 23, 2020. https://digitalcommons.chapman.edu/cads_dissertations/1.

MLA Handbook (7^th Edition):

James, Kevin. “Estimating Auction Equilibria using Individual Evolutionary Learning.” 2019. Web. 23 Feb 2020.

Vancouver:

James K. Estimating Auction Equilibria using Individual Evolutionary Learning. [Internet] [Doctoral dissertation]. Chapman University; 2019. [cited 2020 Feb 23]. Available from: https://digitalcommons.chapman.edu/cads_dissertations/1.

Council of Science Editors:

James K. Estimating Auction Equilibria using Individual Evolutionary Learning. [Doctoral Dissertation]. Chapman University; 2019. Available from: https://digitalcommons.chapman.edu/cads_dissertations/1

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Open Access Theses and Dissertations