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Statistical exploratory analysis of mask-fill reproduction operators of Genetic Algorithms
Applied Soft Computing ( IF 5.472 ) Pub Date : 2021-01-09 , DOI: 10.1016/j.asoc.2021.107087
Hari Mohan Pandey; Marcello Trovati; Nik Bessis

To be successful, a search algorithm needs to balance exploration and exploitation. In Genetic Algorithms (GAs) this is achieved through proportionate selection of individuals and reproduction operators. GAs can suffer premature convergence, when the diversity of the population decreases over time and search gets trapped in a local optimum, returning a very poor quality solution. Mask-fill reproduction operators utilizes bit-masking oriented data structure (BMODS) that maintains a good ratio between exploration and exploitation. Mask-fill reproduction operators have been utilized in various applications, but the exploration and exploitation ability of mask-fill reproduction operators have not been compared against other reproduction operators. This paper describes a rigorous and practical statistical methodology for the exploratory analysis of the mask-fill reproduction operators. First, the issues of robust experimental design and setting the control parameters for implementing a GA is addressed. Second, the impact of various reproduction operator combinations are analysed. In this study, three crossover operators and five mutation operators are considered which creates fifteen crossover–mutation operator combinations. Third, the methodology is demonstrated by considering grammatical inference (GI) problem as domain of enquiry. A hybrid genetic algorithm integrated with Sequitur algorithm (GAWS) is proposed for GI. Numerical results are presented to describe the effect of crossover–mutation operator combinations. The performance of the proposed GAWS is compared against the state-of-the-art algorithms. Statistical test are conducted to determine the performance significance of both crossover–mutation operator combinations and the proposed GAWS algorithm.



中文翻译:

遗传算法掩膜填充算子的统计探索性分析

为了获得成功,搜索算法需要平衡探索和开发。在遗传算法(GA)中,这是通过按比例选择个体和繁殖者来实现的。当种群的多样性随着时间的流逝而减少并且搜索陷入局部最优状态时,GA可能会过早收敛,从而导致质量很差的解决方案。掩码填充再现运算符利用面向位掩码的数据结构(BMODS),该结构在探索和利用之间保持良好的比率。掩模填充再现操作员已经在各种应用中被利用,但是掩模填充再现操作员的探索和开发能力尚未与其他再现操作员进行比较。本文介绍了一种严格而实用的统计方法,用于对口罩填充复制操作符进行探索性分析。首先,解决了鲁棒性实验设计和设置用于实施GA的控制参数的问题。其次,分析了各种复制操作员组合的影响。在这项研究中,考虑了三个交叉算子和五个突变算子,它们创建了十五个交叉-变异算子组合。第三,通过将语法推断(GI)问题视为查询领域来论证该方法。提出了一种与遗传算法集成的混合遗传算法。数值结果表明了交叉变异算子组合的影响。将拟议的GAWS的性能与最新算法进行比较。进行统计测试以确定交叉变异算子组合和所提出的GAWS算法的性能重要性。

更新日期:2021-01-13
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