DocumentCode :
3282437
Title :
Differential Evolution with Graph-Based Speciation by Competitive Hebbian Rules
Author :
Takahama, Tetsuyuki ; Sakai, Shin´ichi
Author_Institution :
Dept. of Intell. Syst., Hiroshima City Univ., Hiroshima, Japan
fYear :
2012
fDate :
25-28 Aug. 2012
Firstpage :
445
Lastpage :
448
Abstract :
Differential evolution (DE) is an evolutionary algorithm and has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multimodal functions. However, it is still difficult to solve hard problems such as multimodal problems and problems with ridge structures. in this study, we propose a new speciation method "graph-based speciation" to keep the diversity of the search points and realize the global search. Also, we utilize the species-best strategy that can realize the global search using speciation and the local search around the seeds of species. It is expected that the efficiency and the robustness of DE can be improved by using the strategy. the advantage of the proposed method is shown by solving some benchmark problems including multimodal problems and problems with ridge structures.
Keywords :
Hebbian learning; concave programming; evolutionary computation; graph theory; nonlinear functions; search problems; competitive Hebbian rules; differential evolution; evolutionary algorithm; global search; graph-based speciation; local search; multimodal functions; multimodal problems; nonconvex functions; nondifferentiable functions; nonlinear functions; optimization problems; ridge structures; search points; species-best strategy; Evolutionary computation; Optimization; Robustness; Search problems; Sociology; Statistics; Vectors; Delaunay diagram; competitive Hebbian rules; differential evolution; speciation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genetic and Evolutionary Computing (ICGEC), 2012 Sixth International Conference on
Conference_Location :
Kitakushu
Print_ISBN :
978-1-4673-2138-9
Type :
conf
DOI :
10.1109/ICGEC.2012.83
Filename :
6457121
Link To Document :
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