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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2005.11
- 수록면
- 51 - 63 (13page)
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초록· 키워드
오류제보하기
When we obtain an optimal solution using GA (Genetic Algorithm), operation such as crossover, reproduction, and mutation procedures is using to generate for the next generations. In this case, it is possible to obtain local solution because chromosomes or individuals which have only a close affinity can convergent To improve an optimal learning solution of GA, this paper deal with applying PSO (Particle Swarm Optimization) and Euclidian data distance to mutation procedure on GA's differentiation. Through this approaches, we can have global and local optimal solution together, and the faster and the exact optimal solution without any local solution. Four test functions are used for proof of this suggested algorithm.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Euclidian distance for GA-PSO
Ⅲ. Improvement of optimal learning of GA using Euclidian distance
Ⅳ. Simulation and Results
Ⅴ. Conclusion
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2009-566-016511682