Stochastic ranking for constrained evolutionary optimization
Stochastic ranking for constrained evolutionary optimization
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DOI：
10.1109/4235.873238
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发表时间：
2000-09-01
影响因子：
14.3
通讯作者：
Yao, X
中科院分区：
文献类型：
--
作者：
Runarsson, TP;Yao, X
惩罚功能通常用于约束优化。但是，很难在目标和惩罚功能之间取得适当的平衡，本文引入了一种新颖的方法，以随机平衡目标和惩罚函数，即随机排名，并根据惩罚功能方法提出了新的观点在这些术语中讨论了惩罚和客观功能的主导地位，幼稚的惩罚方法的某些陷阱。使用（MU，Lambda）进化策略在13个基准问题上测试了新的排名方法。我们的结果表明，仅适当的排名（即选择），而无需引入复杂和专业的变异操作员，能够显着改善搜索性能。
Penalty functions are often used in constrained optimization. However, it is very difficult to strike the right balance between objective and penalty functions, This paper introduces a novel approach to balance objective and penalty functions stochastically, i.e,, stochastic ranking, and presents a new view on penalty function methods in terms of the dominance of penalty and objective functions, Some of the pitfalls of naive penalty methods are discussed in these terms. The new ranking method is tested using a (mu, lambda) evolution strategy on 13 benchmark problems. Our results show that suitable ranking alone (i.e., selection), without the introduction of complicated and specialized variation operators, is capable of improving the search performance significantly.