Acceleration techniques for evolutionary algorithms = 진화연산의 가속기법 연구

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This thesis proposes acceleration techniques for Evolutionary Algorithms(EA), which are the hybrid algorithm and ES with Hessian Covariance(ES-HC). Hybrid algorithm combines evolutionary algorithms(EA) and the gradient search technique, for optimization with continuous parameters. Inheriting the advantages of the two approaches, the new method is fast and capable of global search. The main structure of the new method is similar to that of EA except that a special individual called gradient individual is introduced and EA individuals are located symmetrically. The gradient individual is propagated through generations by means of the quasi-Newton method. Gradient information required for the quasi-Newton method is calculated from the costs of EA individuals produced by the evolution strategies(ES). The symmetric placement of the individuals with respect to the best individual is for calculating the gradient vector by the central difference method. For the estimation of the inverse Hessian matrix, Symmetric Rank-One update shows better performance than the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and Davidon-Fletcher- Powell(DFP) . ES-HC also uses Hessian information. It generates individuals with covariance calculated from decomposition of inverse Hessian matrix without using the gradient individual. Numerical test on various benchmark problems and a practical control design example demonstrate that the proposed methods give faster convergence rate than EA, without sacrificing capability of global search.
Advisors
Tahk, Min-Jearesearcher탁민제researcher
Description
한국과학기술원 : 항공우주공학전공,
Publisher
한국과학기술원
Issue Date
2007
Identifier
263545/325007  / 000965245
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 항공우주공학전공, 2007.2, [ ix, 93 p. ]

Keywords

Hybrid Algorithm; Evolution Strategies; 진화알고리듬; 하이브리드 알고리듬; Evolutionary Algorithms; 진화전략

URI
http://hdl.handle.net/10203/26348
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=263545&flag=dissertation
Appears in Collection
AE-Theses_Ph.D.(박사논문)
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