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Structural Bias in Differential Evolution: a Preliminary Study

机译:差动演进中的结构偏差:初步研究

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This paper extends the study of structural bias in popular metaheuristic global optimisation methods. Previously, it has been shown that both Genetic Algorithms and Particle Swarm Optimisation suffer from such bias. This means that difficulties already posed for a structurally biased algorithm by the fitness landscape itself are further unnecessarily exacerbated by the unexpected oversampling of some regions of the search space and avoidance of the others, to potential great detriment of the overall optimisation performance. Such bias is inherent in the core design of the algorithm. After careful examination, the authors conclude that some variants of Differential Evolution are not free of the structural bias. However, investigation suggests that the mechanisms of the formation of structural bias in Differential Evolution is different and can be balanced through a more careful design.
机译:本文扩展了对流行的成交全球优化方法中结构偏差的研究。以前,已经表明遗传算法和粒子群优化均遭受这种偏差。这意味着通过健身景观本身的结构偏置算法已经提出的困难是不必要地加剧了搜索空间的某些区域的意外过采样和避免其他区域的意外的过采样,以潜在地损害整体优化性能。这种偏置是算法的核心设计中固有的。仔细考试后,作者得出结论,差分进化的一些变体不是没有结构偏差。然而,调查表明,在差分演化中形成结构偏差的机制是不同的,可以通过更仔细的设计来平衡。

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