遗传算法修改:增加种群改进阶段

V. Torchinskii, O. Logunova, N. Sibileva, P. Romanov
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引用次数: 2

摘要

该算法的遗传改进是基于引入一个新的阶段,即通过比特向量的反转来改进个体,并消除突变阶段。个体改进的一个必要属性是所选个体之间的汉明距离度量超出n/2以上,其中n为汉明距离,在比特表示中与个体中基因的数量相同。在这种情况下,“非理想”个体越“差”,反转后就会变得越“好”。部分地,这是通过消除突变阶段来补偿的,并且由于提高收敛速度而实现了速度的总体效果。对经典遗传算法的改进使收敛率提高了25-35%,算法速度提高了15-25%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic algorithm modification: addition of the population improvement stage
The genetic modification of the algorithm is based on the introduction of a new stage consisting in improving the individual by inversion of the bit vector and eliminating the mutation stage. A necessary attribute of the improvement of the individual is the exceeding of the measure of the Hamming distance between the selected individuals by more than n/2, where n is the Hamming distance, which in the bit representation is the same as the number of genes in the individual. In this case, the "worse" the "non-ideal" individual is, the "better" it becomes after the inversion. Partially, this is compensated by the elimination of the mutation stage, and the overall effect in speed is achieved due to increasing the rate of convergence. The proposed improvements to the classical genetic algorithm allow increasing the convergence rate by 25-35% and the algorithm speed by 15-25%.
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