2020년도 인터넷정보학회 추계학술발표대회
Current Result Document :
| 한글제목(Korean Title) |
An Ensemble Differential Evolution and Estimation of Distribution Algorithm |
| 영문제목(English Title) |
An Ensemble Differential Evolution and Estimation of Distribution Algorithm |
| 저자(Author) |
판더빈
조한규
이재완
Debin Fan
Han-Gue Jo
Jaewan Lee
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| 원문수록처(Citation) |
VOL 21 NO. 02 PP. 0377 ~ 0378 (2020. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Estimation of distribution algorithm (EDA) is a well-known population-based evolutionary algorithm (EA). EDA has been widely used in various optimization problems. However, it is easy to produce the problem of premature convergence. Therefore, this paper presents an ensemble differential evolution (DE) and estimation of distribution algorithm (EDEEDA). The proposed EDEEDA algorithm adopts an ensemble strategy to take advantage of DE and EDA. To enhance the diversity of the population, re-mutation, crossover, and selection operations are performed. Twenty-eight benchmark functions of CEC2013 have been conducted to evaluate the performance of EDEEDA. The experimental results show that EDEEDA is an effective ensemble algorithm.
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