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Probability distribution based recombination operator to solve unimodal and multi-modal problems

机译:基于概率分布的重组算子解决单峰和多峰问题

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摘要

The neighborhood-based crossover operators used in real coded genetic algorithm (RCGA) are based on some probability distribution. It is observed that each crossover operator directs the search towards a different zone in the neighborhood of the parents. The quality of the elements that belong to the visited region depends on the particular problems to be solved. Different crossover operators perform differently with respect to the problems, even at the different stages of the genetic process in the same problem. In this paper, the role of probability distribution is empirically investigated on unimodal and multi-modal test problems. It is observed that the operator based on polynomial distribution achieves superior performance on unimodal test problems. The lognormal distribution based operator is efficient in solving multi-modal problems.
机译:实际编码遗传算法(RCGA)中使用的基于邻域的交叉算子基于某种概率分布。可以看到,每个交叉算子都将搜索引向父母附近的不同区域。属于访问区域的元素的质量取决于要解决的特定问题。即使在同一问题的遗传过程的不同阶段,不同的交叉算子对问题的执行也会有所不同。在本文中,通过经验研究了概率分布在单峰和多峰测试问题上的作用。可以看出,基于多项式分布的算子在单峰测试问题上具有优异的性能。基于对数正态分布的算子可有效解决多模式问题。

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