BMODS:一个强大的GA人口的2-D表示方案

Hari Mohan Pandey, Ankit Chaudhary, D. Mehrotra, Yudong Zhang
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引用次数: 0

摘要

遗传算法是一种已经被广泛应用的搜索优化算法,无需介绍。种群的大小、种群的代表性、交叉和突变概率、选择方法等因素对遗传算法的成功有很大的影响。本文讨论了遗传算法的总体表示问题。作者在2001年由Iupsa实现了面向位掩码的数据结构(BMODS)。BMODS是一种有效的方式来存储个体基因组,其中生殖操作已经执行。最近,作者将BMODS纳入语法推理系统,并取得了令人鼓舞的结果。通过本文,目的是展示BMODS对GA总体表示的有用性。
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The BMODS: A powerful 2-D representation scheme for the GA's population
The genetic algorithm is a search an optimization algorithm has been widely used, need no introduction. There exist various factors such as population size, representation of the population, crossover and mutation probabilities, selection method and others greatly contribute to the success of the genetic algorithm. This paper dealt with the representation of the population for the genetic algorithm. The authors have shown the 2-D representation of the population has been called as bit masking oriented data structure (BMODS) was implemented by Iupsa in 2001. The BMODS is an efficient way to store the individual genome in which reproduction operations have been performed. Recently, the authors have incorporated the BMODS for the grammatical inference system and found encouraging results. By this paper, the aim is to show the usefulness of the BMODS for the representation of the GA's population.
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