Genetic algorithm parenting fitness

Q3 Mathematics
M. Ouiss, A. Ettaoufik, A. Marzak, A. Tragha
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引用次数: 0

Abstract

The evolution scheme phase, in which the genetic algorithms select individuals that will form the new population, had an important impact on these algorithms. Many approaches exist in the literature. However, these approaches consider only the value of the fitness function to differenciate best solutions from the worst ones. This article introduces the parenting fitness, a novel parameter, that defines the capacity of an individual to produce fittest offsprings. Combining the standard fitness function and the parenting fitness helps the genetic algorithm to be more efficient, hence, producing best results.
遗传算法亲子适应度
在进化方案阶段,遗传算法选择将形成新种群的个体,对这些算法产生重要影响。文献中存在许多方法。然而,这些方法只考虑适应度函数的值来区分最佳解和最差解。本文介绍了一种新的参数——亲代适合度,它定义了个体产生最适合后代的能力。将标准适应度函数与亲代适应度相结合,有助于提高遗传算法的效率,从而产生最佳结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mathematical Modeling and Computing
Mathematical Modeling and Computing Computer Science-Computational Theory and Mathematics
CiteScore
1.60
自引率
0.00%
发文量
54
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