Search biases in constrained evolutionary optimization
Search biases in constrained evolutionary optimization
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DOI：
10.1109/tsmcc.2004.841906
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发表时间：
2005-05-01
期刊：
影响因子：
--
通讯作者：
Yao, X
中科院分区：
文献类型：
--
作者：
Runarsson, TP;Yao, X
进化优化中约束处理的一种常见方法是应用罚函数来使搜索偏向可行解。有人提出，可以避免主观设置的各种惩罚参数使用多目标制定。本文深入分析和解释了为什么以及何时多目标约束处理方法预期工作或失败。在此基础上，提出了一种基于进化策略和差分变异的改进进化算法.进行了广泛的实验研究。我们的研究结果表明，无偏多目标约束处理的方法可能不像人们可能假设的那样有效。
A common approach to constraint handling in evolutionary optimization is to apply a penalty function to bias the search toward a feasible solution. It has been proposed that the subjective setting of various penalty parameters can be avoided using a multiobjective formulation. This paper analyzes and explains in depth why and when the multiobjective approach to constraint handling is expected to work or fail. Furthermore, an improved evolutionary algorithm based on evolution strategies and differential variation is proposed. Extensive experimental studies have been carried out. Our results reveal that the unbiased multiobjective approach to constraint handling may not be as effective as one may have assumed.