{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-2077"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-2077","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Genetic algorithms using Galib","abstract":"GAlib is a C++ library of genetic algorithm objects that was recently developed at the Massachusetts Institute of Technology. This thesis is to demonstrate its functionality and versatility for implementing haploid tripartite genetic algorithms; We first built a test bed in which GAlib could be used. To achieve this, we used GAlib to solve the Traveling Salesman Problem and implemented two-opt and simulated annealing for compariSon We then examined the use of genetic algorithms for finding loop invariants. We used GAlib successfully to build a model but results remain inconclusive; In our main thrust we applied genetic algorithms to train and develop neural networks. To develop neural network architectures we used two different methods of representing neural networks: connection matrices and graph-generation grammars. We were able to demonstrate that genetic algorithms are an effective tool for training networks as well as for finding network architectures.","abstract_html":"GAlib is a C++ library of genetic algorithm objects that was recently developed at the Massachusetts Institute of Technology. This thesis is to demonstrate its functionality and versatility for implementing haploid tripartite genetic algorithms; We first built a test bed in which GAlib could be used. To achieve this, we used GAlib to solve the Traveling Salesman Problem and implemented two-opt and simulated annealing for compariSon We then examined the use of genetic algorithms for finding loop invariants. We used GAlib successfully to build a model but results remain inconclusive; In our main thrust we applied genetic algorithms to train and develop neural networks. To develop neural network architectures we used two different methods of representing neural networks: connection matrices and graph-generation grammars. We were able to demonstrate that genetic algorithms are an effective tool for training networks as well as for finding network architectures.","abstract_has_math":false,"creators":["Hendricks, Bradley John"],"institution":"University of Nevada, Las Vegas","degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1999,"date_issued":"1999-01-01T08:00:00Z","date_published":"1999-01-01T08:00:00Z","updated_at":"2026-07-24T05:25:04Z","subjects":[],"languages":["English"],"rights":["IN COPYRIGHT. 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We used GAlib successfully to build a model but results remain inconclusive; In our main thrust we applied genetic algorithms to train and develop neural networks. To develop neural network architectures we used two different methods of representing neural networks: connection matrices and graph-generation grammars. We were able to demonstrate that genetic algorithms are an effective tool for training networks as well as for finding network architectures."]},{"key":"dc:format","label":"Dc Format","values":["pdf"]},{"key":"dc:title","label":"Title","values":["Genetic algorithms using Galib"]}]}],"canonical_facts":{"dc:creator":["Hendricks, Bradley John"],"dc:description.abstract":["GAlib is a C++ library of genetic algorithm objects that was recently developed at the Massachusetts Institute of Technology. This thesis is to demonstrate its functionality and versatility for implementing haploid tripartite genetic algorithms; We first built a test bed in which GAlib could be used. To achieve this, we used GAlib to solve the Traveling Salesman Problem and implemented two-opt and simulated annealing for compariSon We then examined the use of genetic algorithms for finding loop invariants. We used GAlib successfully to build a model but results remain inconclusive; In our main thrust we applied genetic algorithms to train and develop neural networks. To develop neural network architectures we used two different methods of representing neural networks: connection matrices and graph-generation grammars. We were able to demonstrate that genetic algorithms are an effective tool for training networks as well as for finding network architectures."],"dc:format":["pdf"],"dc:identifier":["10.25669/4suh-mbmc","https://oasis.library.unlv.edu/rtds/1078","https://oasis.library.unlv.edu/context/rtds/article/2077/viewcontent/uc.pdf"],"dc:language":["English"],"dc:publisher":["University of Nevada, Las Vegas"],"dc:rights":["IN COPYRIGHT. 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