进化算法中实值基因型的限制
On Restricting Real-Valued Genotypes in Evolutionary Algorithms
摘要：
实值基因型与变异算子、变异算子和交叉算子一起构成了进化算法的一些基本构建块。实值基因型被用于广泛的背景下，从人工神经网络中的权重到机器人控制系统中的参数。在实值基因组的大多数应用中，需要将单个参数的范围限制在允许的范围内。在本文中，我们将说明限制实值基因组参数的挑战，并分析最有前途的方法来适当地限制这些值。我们利用经验和基准的例子来证明所提出的方法的实用性，并通过文献综述显示本文的见解如何影响该领域内的其他研究。所提出的方法需要最少的干预，从进化算法的从业者和行为以及反复应用的变异算子，导致更好的理论性能以及在知名的基准显着差异。
英文摘要：
Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks.
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