2020년도 인터넷정보학회 춘계학술발표대회
Current Result Document :
| 한글제목(Korean Title) |
A Self-adaptive Hybrid Differential Evolution with Gaussian Estimation of Distribution Algorithm |
| 영문제목(English Title) |
A Self-adaptive Hybrid Differential Evolution with Gaussian Estimation of Distribution Algorithm |
| 저자(Author) |
판더빈
이재완
덩창서
Debin Fan
Jaewan Lee
Changshou Deng
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| 원문수록처(Citation) |
VOL 21 NO. 01 PP. 0335 ~ 0336 (2020. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This paper proposes a self-adaptive hybrid differential evolution (DE) with Gaussian estimation of distribution algorithm (SDE-GEDA). SDE-GEDA takes advantage of the better global searching ability of estimation of distribution algorithm (EDA) and the strong local searching ability of DE. The proposed algorithm introduces a self-adaptive choice factor to adjust these two algorithms. Meantime, to solve the problems of premature convergence and search stagnation of the algorithm, the most suitable self-adaptive choice factor is selected by the evolutionary state of individuals. To validate the performance of SDE-GEDA, a set of benchmark functions is employed. The experimental results show that the proposed algorithm is effective and achieve better performance than other comparison algorithms.
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