Journal
FRONTIERS OF COMPUTER SCIENCE
Volume 12, Issue 2, Pages 297-315Publisher
HIGHER EDUCATION PRESS
DOI: 10.1007/s11704-016-5353-5
Keywords
differential evolution; mutation; the selection of solutions for mutation; evolutionary algorithms
Categories
Funding
- National Basic Research Program (973 Program) of China [2011CB013104]
- Innovation-driven Plan in Central South University [2015CXS012, 2015CX007]
- National Natural Science Foundation of China [61273314, 61673397]
- EU Horizon Marie Sklodowska-Curie Individual Fellowships [661327]
- Hunan Provincial Natural Science Fund for Distinguished Young Scholars [2016JJ1018]
- Program for New Century Excellent Talents in University [NCET-13-0596]
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology
- Marie Curie Actions (MSCA) [661327] Funding Source: Marie Curie Actions (MSCA)
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Differential evolution (DE) is a kind of evolutionary algorithms, which is suitable for solving complex optimization problems. Mutation is a crucial step in DE that generates new solutions from old ones. It was argued and has been commonly adopted in DE that the solutions selected for mutation should have mutually different indices. This restrained condition, however, has not been verified either theoretically or empirically yet. In this paper, we empirically investigate the selection of solutions for mutation in DE. From the observation of the extensive experiments, we suggest that the restrained condition could be relaxed for some classical DE versions as well as some advanced DE variants. Moreover, relaxing the restrained condition may also be useful in designing better future DE algorithms.
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