动态势交叉算子

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Francisco Chicano;Gabriela Ochoa;L. Darrell Whitley;Renato Tinós
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引用次数: 3

摘要

双亲解的最优重组算子提供了从其中一个亲本获得每个变量值的最优解(基因传递特性)。如果解是位串,则最优重组算子的子代在包含两个父解的最小超平面中是最优的。通常,探索这个超平面的计算成本很高,在最坏的情况下需要指数时间。然而,当目标函数的变量相互作用图是稀疏的时,可以在多项式时间内进行探索。在本文中,我们提出了一种重组算子,称为动态势交叉算子(DPX),它在多项式时间内运行,其行为类似于低上位性组合问题的最优重组算子。我们在理论和实验上将该算子与传统的交叉算子(如均匀交叉和网络交叉)以及最近定义的两种有效重组算子(分割交叉和连接点分割交叉)进行了比较。经验比较使用NKQ Landscapes和MAX-SAT实例。DPX在子代的质量方面优于其他交叉算子,并在轨迹和基于群体的元启发式中提供了更好的结果,但它需要更多的时间和内存来计算子代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynastic Potential Crossover Operator
An optimal recombination operator for two-parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring this hyperplane is computationally costly, in general, requiring exponential time in the worst case. However, when the variable interaction graph of the objective function is sparse, exploration can be done in polynomial time. In this article, we present a recombination operator, called Dynastic Potential Crossover (DPX), that runs in polynomial time and behaves like an optimal recombination operator for low-epistasis combinatorial problems. We compare this operator, both theoretically and experimentally, with traditional crossover operators, like uniform crossover and network crossover, and with two recently defined efficient recombination operators: partition crossover and articulation points partition crossover. The empirical comparison uses NKQ Landscapes and MAX-SAT instances. DPX outperforms the other crossover operators in terms of quality of the offspring and provides better results included in a trajectory and a population-based metaheuristic, but it requires more time and memory to compute the offspring.
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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