- Volume 8 Issue 2
Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.
- Adaptation in Natural and Artificial Systems Holland,J.H.
- Ph. D. thesis, Dept, Civil Eng., Univ. Michigarn Computer-Aided Gas Pipeline Operation using Genetic Algorithms and Rule Learning Goldberg,D.E.
- Genetic Algorithms in Search, Optimization & Machine Learning Goldberg,D.E.
- 전산구조공학 v.5 no.2 전체최적화를 위한 확률론적 탐색기법 양영순;김기화
- 대한조선학회논문집 v.31 no.4 유전적 알고리즘에 의한 선체 구조물의 이산적 최적설계 양영순;김기화;유원선
- 서울대학교 조선해양공학과 박사학위논문 Genetic Algorithm에 의한 다목적함수 최적구조설계 김기화
- Bayesian Approach to Global Optimization Mockus,J.
- Mathematical Programming via Augmented Lagrangians: An introduction with Computer Programs Pierre,D.A.;Lowe,M.J.
- Applied Optimal Design Haug,E.J.;Arora,J.S.
- 대한조선학회지 v.31 no.1 유전적 알고리듬을 이용한 최적 구조 김기화