无参数基因库最优混合进化算法。

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arkadiy Dushatskiy, Marco Virgolin, Anton Bouter, Dirk Thierens, Peter A N Bosman
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引用次数: 10

摘要

当涉及到用进化算法(EAs)以可靠和可扩展的方式解决优化问题时,检测和利用链接信息,即变量之间的依赖关系,可能是关键。在本文中,我们提出了最新版本的基因池最优混合进化算法(gome),并提出了实质性的改进:一个明确设计用于估计和利用连锁信息的EA。我们首先对几个goma设计选择执行大规模搜索,以了解最重要的是什么,并获得通常性能最好的算法版本。接下来,我们介绍了一个新的GOMEA版本,称为GOMEA,其中基于链接的变化通过基于条件依赖关系的过滤解决方案匹配得到进一步改进。我们在广泛的实验评估中比较了最新版本的GOMEA和另一个竞争的链接感知EA DSMGA-II,涉及9个黑盒问题的基准集,只有揭示和利用它们固有的依赖结构才能有效地解决。最后,为了使ea对参数选择的可用性和弹性更强,我们研究了不同的goma和GOMEA自动种群管理方案的性能,实际上使ea无参数化。我们的研究结果表明,在大多数问题上,goma和goma显著优于原来的goma和DSMGA-II,为该领域开创了新的技术水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Parameterless Gene-pool Optimal Mixing Evolutionary Algorithms.

When it comes to solving optimization problems with evolutionary algorithms (EAs) in a reliable and scalable manner, detecting and exploiting linkage information, i.e., dependencies between variables, can be key. In this article, we present the latest version of, and propose substantial enhancements to, the Gene-pool Optimal Mixing Evoutionary Algorithm (GOMEA): an EA explicitly designed to estimate and exploit linkage information. We begin by performing a largescale search over several GOMEA design choices to understand what matters most and obtain a generally best-performing version of the algorithm. Next, we introduce a novel version of GOMEA, called CGOMEA, where linkage-based variation is further improved by filtering solution mating based on conditional dependencies. We compare our latest version of GOMEA, the newly introduced CGOMEA, and another contending linkage-aware EA, DSMGA-II, in an extensive experimental evaluation, involving a benchmark set of 9 black-box problems that can only be solved efficiently if their inherent dependency structure is unveiled and exploited. Finally, in an attempt to make EAs more usable and resilient to parameter choices, we investigate the performance of different automatic population management schemes for GOMEA and CGOMEA, de facto making the EAs parameterless. Our results show that GOMEA and CGOMEA significantly outperform the original GOMEA and DSMGA-II on most problems, setting a new state of the art for the field.

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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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