Penalty Function Methods for Constrained Optimization with Genetic Algorithms
Penalty Function Methods for Constrained Optimization with Genetic Algorithms
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DOI：
10.3390/mca10010045
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发表时间：
2005-04
影响因子：
1.9
通讯作者：
Ozgur Yeniay
中科院分区：
文献类型：
--
作者：
Ozgur Yeniay
遗传算法最直接适用于无约束优化。遗传算法在约束优化问题中的应用是一项具有挑战性的工作。已经提出了几种处理约束的方法。遗传算法中处理约束最常用的方法是使用罚函数。在本文中，我们介绍了这些基于惩罚的方法，并讨论了它们的优缺点。
Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to handle constraints is to use penalty functions. In this paper, we present these penalty-based methods and discuss their strengths and weaknesses.