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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Satishkumar MV. Simultaneous scheduling of machines and Agvs using evolutionary optimization algorithms. [Internet] [Thesis]. Jawaharlal Nehru Technological University; 2010. [cited 2021 Feb 26]. 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

Ryerson University

2. 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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Hajipour Y. Non-periodic inspection of optimization of repairable systems. [Internet] [Thesis]. Ryerson University; 2016. [cited 2021 Feb 26]. 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

Rochester Institute of Technology

3. 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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Lamos-Sweeney J. Deep learning using genetic algorithms. [Internet] [Thesis]. Rochester Institute of Technology; 2012. [cited 2021 Feb 26]. 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

University of Alberta

4. 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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Shu L. The impact of data structures on the performance of genetic-algorithm-based learning. [Internet] [Doctoral dissertation]. University of Alberta; 1992. [cited 2021 Feb 26]. 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

5. 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 26, 2021. 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. 26 Feb 2021.

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 2021 Feb 26]. 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

Texas A&M University

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

Degree: MS, Petroleum Engineering, 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. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399

Chicago Manual of Style (16^th Edition):

Gibbs, Trevor Howard. “Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms.” 2011. Masters Thesis, Texas A&M University. Accessed February 26, 2021. http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399.

MLA Handbook (7^th Edition):

Gibbs, Trevor Howard. “Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms.” 2011. Web. 26 Feb 2021.

Vancouver:

Gibbs TH. Horizontal Well Placement Optimization in Gas Reservoirs Using Genetic Algorithms. [Internet] [Masters thesis]. Texas A&M University; 2011. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2010-05-399.

Council of Science Editors:

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

Nelson Mandela Metropolitan University

7. 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 26, 2021. 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. 26 Feb 2021.

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 2021 Feb 26]. 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

Oregon State University

8. 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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Dill KM. Growing digital circuits : logic synthesis and minimization with genetic operators. [Internet] [Masters thesis]. Oregon State University; 1996. [cited 2021 Feb 26]. 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

9. 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 26, 2021. http://hdl.handle.net/1957/34323.

MLA Handbook (7^th Edition):

Canpolat, Necati. “Optimization of seasonal irrigation scheduling by genetic algorithms.” 1997. Web. 26 Feb 2021.

Vancouver:

Canpolat N. Optimization of seasonal irrigation scheduling by genetic algorithms. [Internet] [Doctoral dissertation]. Oregon State University; 1997. [cited 2021 Feb 26]. 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 Guelph

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

Degree: PhD, School of Computer Science, 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 (6^th Edition):

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

Chicago Manual of Style (16^th Edition):

Brown, Joseph Alexander. “Regression and Classification from Extinction.” 2014. Doctoral Dissertation, University of Guelph. Accessed February 26, 2021. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793.

MLA Handbook (7^th Edition):

Brown, Joseph Alexander. “Regression and Classification from Extinction.” 2014. Web. 26 Feb 2021.

Vancouver:

Brown JA. Regression and Classification from Extinction. [Internet] [Doctoral dissertation]. University of Guelph; 2014. [cited 2021 Feb 26]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/7793.

Council of Science Editors:

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

University of Adelaide

11. 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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Sullivan ND. Task Allocation and Collaborative Localisation in Multi-Robot Systems. [Internet] [Thesis]. University of Adelaide; 2019. [cited 2021 Feb 26]. 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

Chapman University

12. 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 (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 26, 2021. https://digitalcommons.chapman.edu/cads_dissertations/1.

MLA Handbook (7^th Edition):

James, Kevin. “Estimating Auction Equilibria using Individual Evolutionary Learning.” 2019. Web. 26 Feb 2021.

Vancouver:

James K. Estimating Auction Equilibria using Individual Evolutionary Learning. [Internet] [Doctoral dissertation]. Chapman University; 2019. [cited 2021 Feb 26]. 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

Hong Kong University of Science and Technology

13. 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

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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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Law NL. Parameter-free adaptive genetic algorithm. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2007. [cited 2021 Feb 26]. 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

14. 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 (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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Buyukbozkirli B. Modeling genetic algorithm dynamics for OneMax and deceptive functions. [Internet] [Doctoral dissertation]. Michigan State University; 2004. [cited 2021 Feb 26]. 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

15. 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

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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 26, 2021. 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. 26 Feb 2021.

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 2021 Feb 26]. 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

Montana State University

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

Degree: PhD, 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. (Doctoral Dissertation). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/1448

Chicago Manual of Style (16^th Edition):

Hayes, Christina Savannah Maria. “Generic properties of the infinite population genetic algorithm.” 2006. Doctoral Dissertation, Montana State University. Accessed February 26, 2021. https://scholarworks.montana.edu/xmlui/handle/1/1448.

MLA Handbook (7^th Edition):

Hayes, Christina Savannah Maria. “Generic properties of the infinite population genetic algorithm.” 2006. Web. 26 Feb 2021.

Vancouver:

Hayes CSM. Generic properties of the infinite population genetic algorithm. [Internet] [Doctoral dissertation]. Montana State University; 2006. [cited 2021 Feb 26]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/1448.

Council of Science Editors:

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

Montana State University

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

Degree: MS, 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. (Masters Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/7586

Chicago Manual of Style (16^th Edition):

Pamplin, Trenton L. “Hybridizing statistics with genetic algorithms.” 1995. Masters Thesis, Montana State University. Accessed February 26, 2021. https://scholarworks.montana.edu/xmlui/handle/1/7586.

MLA Handbook (7^th Edition):

Pamplin, Trenton L. “Hybridizing statistics with genetic algorithms.” 1995. Web. 26 Feb 2021.

Vancouver:

Pamplin TL. Hybridizing statistics with genetic algorithms. [Internet] [Masters thesis]. Montana State University; 1995. [cited 2021 Feb 26]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/7586.

Council of Science Editors:

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

18. 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 (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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Henshall G. Optimising kinematic systems using crowd-sourcing and genetic algorithms. [Internet] [Doctoral dissertation]. Bangor University; 2019. [cited 2021 Feb 26]. 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 New South Wales

19. 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 (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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Chi T. Evolutionary adaptation and optimisation in heterogeneous and homogeneous aerial search vehicles. [Internet] [Masters thesis]. University of New South Wales; 2015. [cited 2021 Feb 26]. 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

University of Oklahoma

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

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 26, 2021. 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. 26 Feb 2021.

Vancouver:

Carlson B. Phenotype Operators for Improved Performance of Heuristic Encoding Within Genetic Algorithms. [Internet] [Doctoral dissertation]. University of Oklahoma; 2016. [cited 2021 Feb 26]. 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

University of Missouri – Columbia

21. Shi, Zhuoqun. Improved genetic algorithm for distribution system performance analysis.

Degree: 2017, University of Missouri – Columbia

URL: http://hdl.handle.net/10355/62087

► The incipient smart grid initiative and increasing use of distributed generation, along with classical problems of distribution system reconfiguration and restoration (DSR), have led to… (more)

▼ The incipient smart grid initiative and increasing use of distributed generation, along with classical problems of distribution system reconfiguration and restoration (DSR), have led to the need for efficient and reliable power distribution system simulation tools. One recently developed tool, the Fast Non-dominated Sorting Genetic Algorithm, or FNSGA, has been shown to be very effective at finding Pareto optimal distribution systems that are optimized with respect to voltages, currents, and power losses. Despite its promise, the FNSGA has two shortcomings, which are addressed in this thesis. The first is that it uses a load flow sub-program (to determine voltages, currents, and losses throughout the power system in question) that is based on the classical Newton-Raphson numerical analytical approach. For a variety of reasons, the Newton-Raphson method often encounters convergence problems when applied to distribution (rather than transmission) systems, and it has burdensome memory and CPU time requirements when applied to large systems. In this thesis, the Newton-Raphson load flow program is replaced in the FNSGA with a revised version of the so-called direct method, the principal revision being a coded novel scheme for properly, rapidly, and repeatedly re-numbering the busses and branches in the power system for load flow analysis within the FNSGA. Validation results for the described scheme are presented by comparing results obtained with it and with the Newton-Raphson-based load flow scheme. The principal difference between the two methods is that the computation time is significantly reduced with the revised direct load flow method. The second shortcoming of the FNSGA to be addressed here is that it is not optimized for certain important parameters, namely the initial population size N and the number of generations Gen, which could lead to excessive CPU time requirements. In this thesis, a parametric study was conducted to determine minimum values of N and Gen that lead to reasonably repeatable configurations of a distribution system that are optimized for the multiple objectives of voltages (in the voltage profiles), currents (in the system load balancing index), and power losses. Studies conducted on 16- and 32-bus test systems revealed that, to produce repeatable solution sets in the 16-bus system, optimum values of N and Gen are small enough that CPU times are very small. However, in the 32-bus system, N and Gen need to be so large that CPU times become prohibitive. Presumably, the problem would get worse with even larger systems. Fortunately, a solution to this problem was found, which involves removing certain branches from the pool of possibilities when producing the initial population N in the genetic algorithm. Disqualified branches are those determined in preliminary simulations to never appear in Pareto optimal solution sets. This method was shown to be very effective at leading to small enough optimum values of N and Gen that CPU times are reasonable. Advisors/Committee Members: O'Connell, Robert (advisor).

Subjects/Keywords: Genetic algorithms; Performance – Analysis

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

APA (6^th Edition):

Shi, Z. (2017). Improved genetic algorithm for distribution system performance analysis. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/62087

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):

Shi, Zhuoqun. “Improved genetic algorithm for distribution system performance analysis.” 2017. Thesis, University of Missouri – Columbia. Accessed February 26, 2021. http://hdl.handle.net/10355/62087.

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

MLA Handbook (7^th Edition):

Shi, Zhuoqun. “Improved genetic algorithm for distribution system performance analysis.” 2017. Web. 26 Feb 2021.

Vancouver:

Shi Z. Improved genetic algorithm for distribution system performance analysis. [Internet] [Thesis]. University of Missouri – Columbia; 2017. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/10355/62087.

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

Council of Science Editors:

Shi Z. Improved genetic algorithm for distribution system performance analysis. [Thesis]. University of Missouri – Columbia; 2017. Available from: http://hdl.handle.net/10355/62087

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

Ryerson University

22. 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 February 26, 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. 26 Feb 2021.

Vancouver:

Jirasek L. A genetic algorithm based partition generation and optimization method for finite element problems. [Internet] [Thesis]. Ryerson University; 2009. [cited 2021 Feb 26]. 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

23. 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 February 26, 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. 26 Feb 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 Feb 26]. 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

24. 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 (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 February 26, 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. 26 Feb 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 Feb 26]. 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

25. Jose, Sonu. Bayesian Network Structure Learning and its Application on Simulation-based Learning.

Degree: 2020, University of Nevada – Reno

URL: http://hdl.handle.net/11714/7674

► Bayesian network is a probabilistic graphical model that has wide applications in various domains due to its peculiarity of knowledge representation and reasoning under uncertainty.… (more)

▼ Bayesian network is a probabilistic graphical model that has wide applications in various domains due to its peculiarity of knowledge representation and reasoning under uncertainty. This research aims at Bayesian network structure learning and how the learned model can be used for reasoning. Learning the structure of Bayesian network from data is a challenging task because the number of possible structures increases super-exponentially with the increase in the number of variables. In order to address this challenge, we develop a method to efficiently learn the Bayesian network structure underlying the data set. Our novel hybrid Structure Learning Algorithm (hybrid-SLA) consists of two phases: the constraint-based phase restricts the search space and a score-and-search phase evolves the Bayesian network structure by exploring the reduced search space. In this dissertation, firstly, we investigate the effectiveness of using genetic algorithms as a score-and-search approach in our hybrid method. We evaluate the performance by comparing our proposed approach with the state-of-the-art algorithms over various well-known benchmark networks. The experimental results illustrate that our approach achieves good performance in inferring the most probable structure for the fully observed data. Secondly, we introduce a hybrid approach where we incorporate case-injected genetic algorithms as a score-and-search method that uses a caching mechanism for inducing Bayesian network structure. Our case-injected genetic algorithm enhance the performance over a sequence of similar problems by extracting and storing knowledge from previously solved problems and utilizing the accumulated knowledge in order to avoid repeated computations when solving subsequent related problems. To evaluate the viability of our approach, we conducted a series of experiments by generating a sequence of similar problems based on using data sets obtained randomly from various popular benchmark networks. Results show that our hybrid approach with case-injected genetic algorithm outperforms the genetic algorithm and another state-of-the-art algorithm in learning the Bayesian network structure. Finally, we apply our hybrid method to a simulation-based learning domain to perform structure discovery and to analyze the accuracy of the learned model in inferring the students’ difficult-to-learn concepts. Results show that the learned Bayesian network model is efficient in identifying such concepts. Advisors/Committee Members: Dascalu, Sergiu (advisor), Louis, Sushil (advisor), Liu, Siming (committee member), Elfass, Sherif (committee member), Usinger, Janet (committee member).

Subjects/Keywords: Bayesian networks; Case-injected Genetic Algorithms; Genetic Algorithms; Inference; Structure Learning

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APA (6^th Edition):

Jose, S. (2020). Bayesian Network Structure Learning and its Application on Simulation-based Learning. (Thesis). University of Nevada – Reno. Retrieved from http://hdl.handle.net/11714/7674

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):

Jose, Sonu. “Bayesian Network Structure Learning and its Application on Simulation-based Learning.” 2020. Thesis, University of Nevada – Reno. Accessed February 26, 2021. http://hdl.handle.net/11714/7674.

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

MLA Handbook (7^th Edition):

Jose, Sonu. “Bayesian Network Structure Learning and its Application on Simulation-based Learning.” 2020. Web. 26 Feb 2021.

Vancouver:

Jose S. Bayesian Network Structure Learning and its Application on Simulation-based Learning. [Internet] [Thesis]. University of Nevada – Reno; 2020. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/11714/7674.

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

Council of Science Editors:

Jose S. Bayesian Network Structure Learning and its Application on Simulation-based Learning. [Thesis]. University of Nevada – Reno; 2020. Available from: http://hdl.handle.net/11714/7674

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

University of Stirling

26. Neumann, Geoffrey K. TEDA: A Targeted Estimation of Distribution Algorithm.

Degree: PhD, School of Natural Sciences, 2014, University of Stirling

URL: http://hdl.handle.net/1893/20248

► This thesis discusses the development and performance of a novel evolutionary algorithm, the Targeted Estimation of Distribution Algorithm (TEDA). TEDA takes the concept of targeting,… (more)

▼ This thesis discusses the development and performance of a novel evolutionary algorithm, the Targeted Estimation of Distribution Algorithm (TEDA). TEDA takes the concept of targeting, an idea that has previously been shown to be effective as part of a Genetic Algorithm (GA) called Fitness Directed Crossover (FDC), and introduces it into a novel hybrid algorithm that transitions from a GA to an Estimation of Distribution Algorithm (EDA). Targeting is a process for solving optimisation problems where there is a concept of control points, genes that can be said to be active, and where the total number of control points found within a solution is as important as where they are located. When generating a new solution an algorithm that uses targeting must ﬁrst of all choose the number of control points to set in the new solution before choosing which to set. The hybrid approach is designed to take advantage of the ability of EDAs to exploit patterns within the population to effectively locate the global optimum while avoiding the tendency of EDAs to prematurely converge. This is achieved by initially using a GA to effectively explore the search space before transitioning into an EDA as the population converges on the region of the global optimum. As targeting places an extra restriction on the solutions produced by specifying their size, combining it with the hybrid approach allows TEDA to produce solutions that are of an optimal size and of a higher quality than would be found using a GA alone without risking a loss of diversity. TEDA is tested on three different problem domains. These are optimal control of cancer chemotherapy, network routing and Feature Subset Selection (FSS). Of these problems, TEDA showed consistent advantage over standard EAs in the routing problem and demonstrated that it is able to ﬁnd good solutions faster than untargeted EAs and non evolutionary approaches at the FSS problem. It did not demonstrate any advantage over other approaches when applied to chemotherapy. The FSS domain demonstrated that in large and noisy problems TEDA’s targeting derived ability to reduce the size of the search space signiﬁcantly increased the speed with which good solutions could be found. The routing domain demonstrated that, where the ideal number of control points is deceptive, both targeting and the exploitative capabilities of an EDA are needed, making TEDA a more effective approach than both untargeted approaches and FDC. Additionally, in none of the problems was TEDA seen to perform signiﬁcantly worse than any alternative approaches.

Subjects/Keywords: Evolutionary Algorithms; Genetic Algorithms; Estimation of Distribution Algorithms; Targeting; TEDA; FDC; Distribution (Probability theory); Genetic algorithms; Algorithms

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

APA (6^th Edition):

Neumann, G. K. (2014). TEDA: A Targeted Estimation of Distribution Algorithm. (Doctoral Dissertation). University of Stirling. Retrieved from http://hdl.handle.net/1893/20248

Chicago Manual of Style (16^th Edition):

Neumann, Geoffrey K. “TEDA: A Targeted Estimation of Distribution Algorithm.” 2014. Doctoral Dissertation, University of Stirling. Accessed February 26, 2021. http://hdl.handle.net/1893/20248.

MLA Handbook (7^th Edition):

Neumann, Geoffrey K. “TEDA: A Targeted Estimation of Distribution Algorithm.” 2014. Web. 26 Feb 2021.

Vancouver:

Neumann GK. TEDA: A Targeted Estimation of Distribution Algorithm. [Internet] [Doctoral dissertation]. University of Stirling; 2014. [cited 2021 Feb 26]. Available from: http://hdl.handle.net/1893/20248.

Council of Science Editors:

Neumann GK. TEDA: A Targeted Estimation of Distribution Algorithm. [Doctoral Dissertation]. University of Stirling; 2014. Available from: http://hdl.handle.net/1893/20248

27. Hafnaoui, Imane. Multimodal biometric fusion using evolutionary techniques.

Degree: 2014, Université M'Hamed Bougara Boumerdès

URL: http://dlibrary.univ-boumerdes.dz:8080123456789/1592

►

75 p. : ill. ; 30 cm

The work of this research focuses on fusing multiple biometric modalities at the score level using different combination… (more)

Subjects/Keywords: Genetic algorithms; Hybride; Fusion; Algorithmes génétiques

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

APA (6^th Edition):

Hafnaoui, I. (2014). Multimodal biometric fusion using evolutionary techniques. (Thesis). Université M'Hamed Bougara Boumerdès. Retrieved from http://dlibrary.univ-boumerdes.dz:8080123456789/1592

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):

Hafnaoui, Imane. “Multimodal biometric fusion using evolutionary techniques.” 2014. Thesis, Université M'Hamed Bougara Boumerdès. Accessed February 26, 2021. http://dlibrary.univ-boumerdes.dz:8080123456789/1592.

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

MLA Handbook (7^th Edition):

Hafnaoui, Imane. “Multimodal biometric fusion using evolutionary techniques.” 2014. Web. 26 Feb 2021.

Vancouver:

Hafnaoui I. Multimodal biometric fusion using evolutionary techniques. [Internet] [Thesis]. Université M'Hamed Bougara Boumerdès; 2014. [cited 2021 Feb 26]. Available from: http://dlibrary.univ-boumerdes.dz:8080123456789/1592.

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

Council of Science Editors:

Hafnaoui I. Multimodal biometric fusion using evolutionary techniques. [Thesis]. Université M'Hamed Bougara Boumerdès; 2014. Available from: http://dlibrary.univ-boumerdes.dz:8080123456789/1592

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

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

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 February 26, 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. 26 Feb 2021.

Vancouver:

Brameier M. On linear genetic programming. [Internet] [Thesis]. Universität Dortmund; 2004. [cited 2021 Feb 26]. 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

29. Chan, Che-Hang Cliff. Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm.

Degree: 2003, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A478

► The thesis presents a Genetic Algorithm with Adaptive Search Space (GAASS) proposed to improve both convergence performance and solution accuracy of traditional Genetic Algorithms(GAs). The… (more)

Subjects/Keywords: Genetic algorithms; Hysteresis

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

APA (6^th Edition):

Chan, C. C. (2003). Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A478

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):

Chan, Che-Hang Cliff. “Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm.” 2003. Thesis, Ryerson University. Accessed February 26, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A478.

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

MLA Handbook (7^th Edition):

Chan, Che-Hang Cliff. “Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm.” 2003. Web. 26 Feb 2021.

Vancouver:

Chan CC. Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm. [Internet] [Thesis]. Ryerson University; 2003. [cited 2021 Feb 26]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A478.

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

Council of Science Editors:

Chan CC. Actuator hysteresis modeling and compensation with an adaptive search space based genetic algorithm. [Thesis]. Ryerson University; 2003. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A478

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

Ryerson University

30. Sivapalan, Sanjeevan. A genetic algorithm approach to recommender system cold start problem.

Degree: 2015, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A3664

► Recommender systems (RS) are ubiquitous and used in many systems to augment user experience to improve usability and they achieve this by helping users discover… (more)

Subjects/Keywords: Recommender systems (Information filtering); Genetic algorithms.

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APA (6^th Edition):

Sivapalan, S. (2015). A genetic algorithm approach to recommender system cold start problem. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A3664

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):

Sivapalan, Sanjeevan. “A genetic algorithm approach to recommender system cold start problem.” 2015. Thesis, Ryerson University. Accessed February 26, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A3664.

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

MLA Handbook (7^th Edition):

Sivapalan, Sanjeevan. “A genetic algorithm approach to recommender system cold start problem.” 2015. Web. 26 Feb 2021.

Vancouver:

Sivapalan S. A genetic algorithm approach to recommender system cold start problem. [Internet] [Thesis]. Ryerson University; 2015. [cited 2021 Feb 26]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A3664.

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

Council of Science Editors:

Sivapalan S. A genetic algorithm approach to recommender system cold start problem. [Thesis]. Ryerson University; 2015. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A3664

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

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Open Access Theses and Dissertations