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CEC
2007
IEEE

Performance tuning of genetic algorithms with reserve selection

8 years 9 months ago
Performance tuning of genetic algorithms with reserve selection
Abstract— This paper provides a deep insight into the performance of genetic algorithms with reserve selection (GARS), and investigates how parameters can be regulated to solve optimization problems more efficiently. First of all, we briefly present GARS, an improved genetic algorithm with a reserve selection mechanism which helps to avoid premature convergence. The comparable results to state-of-the-art techniques such as fitness scaling and sharing demonstrate both the effectiveness and the robustness of GARS in global optimization. Next, two strategies named Static RS and Dynamic RS are proposed for tuning the parameter reserve size to optimize the performance of GARS. Empirical studies conducted in several cases indicate that the optimal reserve size is problem dependent.
Yang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CEC
Authors Yang Chen, Jinglu Hu, Kotaro Hirasawa, Songnian Yu
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