Optimising multi-modal polynomial mutation operators for multi-objective problem classes
Optimising multi-modal polynomial mutation operators for multi-objective problem classes
复制标题
针对多目标问题类优化多模态多项式变异算子
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
E. Keedwell
中科院分区：
文献类型：
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作者：
Kent McClymont;E. Keedwell
本文提出了一种新的方法，生成新的概率分布，适合于特定的问题类，用于优化变异算子。使用所提出的技术创建了一系列具有不同行为的量身定制的算子，并且发现当应用于简单的（1+1）进化策略中的突变算子时，进化的多模态多项式分布与调谐高斯分布的性能相匹配。所生成的算法被示出为显示DTLZ测试问题1、2和7的期望特性的范围，例如收敛速度。
This paper presents a novel method of generating new probability distributions tailored to specific problem classes for use in optimisation mutation operators. A range of tailored operators with varying behaviours are created using the proposed technique and the evolved multi-modal polynomial distributions are found to match the performance of a tuned Gaussian distribution when applied to a mutation operator incorporated in a simple (1+1) Evolution Strategy. The generated heuristics are shown to display a range of desirable characteristics for the DTLZ test problems 1, 2 and 7; such as speed of convergence.