Hybrid genetic algorithms with reduced premature convergence for search performance enhancement탐색 성능 향상을 위한 감소된 조기 집중 현상을 갖는 하이브리드 유전 알고리즘

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dc.contributor.advisorLee, Ju-Jang-
dc.contributor.advisor이주장-
dc.contributor.authorJeong, Il-Kwon-
dc.contributor.author정일권-
dc.date.accessioned2011-12-14-
dc.date.available2011-12-14-
dc.date.issued1999-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=150998&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/36497-
dc.description학위논문(박사) - 한국과학기술원 : 전기및전자공학과, 1999.2, [ vi, 108 p. ]-
dc.description.abstractThis thesis deals with the search performance enhancement techniques in genetic algorithms (GAs). Evolutionary algorithms are computational optimization techniques that use simulated evolution. In control area, especially the genetic algorithm has been widely used. Although GAs are good at finding near global optimum quickly, they are poor in the fine tuning of solutions, which may cause `premature convergence``. This thesis is aimed to improve the search performance of GAs with elitist strategy by reducing premature convergence through hybridization or appropriate modification to the algorithms. In order to accomplish the goals, three new genetic algorithms and two new local search operators are proposed. The first method, the modified genetic algorithm (MGA) consists of a fitness modification scheme and adaptive mutation operator. The second method, the adaptive genetic algorithm (AGA) determines crossover and mutation probabilities by itself according to the fitness of a solution to be crossed or mutated. The schema theorem for AGA is derived. The third method, adaptive simulated annealing genetic algorithm (ASAGA) uses simulated annealing-like mutation operator. A novel way of generating a new solution by using a gaussian random number with time-varying variance is proposed and proved to be effective. The first local search operator makes use of neural networks. The second one named SLSO (simple local search operator) uses the difference between the most recent best fitness and a newly found best solution``s fitness, which is computationally simpler than the first one and proved to be powerful. The test problems considered for the performance comparison include the traditional set of test functions, system identification, neural network controller for cart-pole system, evolutionary design of a multi-agents playing a simplified soccer and nonlinear constrained optimization problems. Nine hybrid genetic algorithms are constructed using the proposed algorithms...eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectLocal search operator-
dc.subjectPremature convergence-
dc.subjectHybrid genetic algorithm-
dc.subjectSimulated annealing-
dc.subject근사 담금질-
dc.subject지역 탐색 연산자-
dc.subject조기 집중 현상-
dc.subject하이브리드 유전 알고리즘-
dc.titleHybrid genetic algorithms with reduced premature convergence for search performance enhancement-
dc.title.alternative탐색 성능 향상을 위한 감소된 조기 집중 현상을 갖는 하이브리드 유전 알고리즘-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN150998/325007-
dc.description.department한국과학기술원 : 전기및전자공학과, -
dc.identifier.uid000945395-
dc.contributor.localauthorLee, Ju-Jang-
dc.contributor.localauthor이주장-
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EE-Theses_Ph.D.(박사논문)
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