Coevolutionary strategies at the collective level for improved generalism

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2023-02-06 DOI:10.1017/dce.2023.1
P. Grudniewski, A. Sobey
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引用次数: 1

Abstract

Abstract In many complex practical optimization cases, the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialized approach to each application. The previously developed multilevel selection genetic algorithm (MLSGA) already shows good performance on a range of problems due to its diversity-first approach, which is rare among evolutionary algorithms. To increase the generality of its performance, this paper proposes utilization of multiple distinct evolutionary strategies simultaneously, similarly to algorithm selection, but with coevolutionary mechanisms between the subpopulations. This distinctive approach to coevolution provides less regular communication between subpopulations with competition between collectives rather than individuals. This encourages the collectives to act more independently creating a unique subregional search, leading to the development of coevolutionary MLSGA (cMLSGA). To test this methodology, nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The mechanisms are tested on 100 different functions and benchmarked against the 9 state-of-the-art competitors to evaluate the generality of each approach. The results show that the diversity divergence in the principles of working of the selected coevolutionary approaches is more important than their individual performances. The proposed methodology has the most uniform performance on the divergent problem types, from across the tested state of the art, leading to an algorithm more likely to solve complex problems with limited knowledge about the search space, but is outperformed by more specialized solvers on simpler benchmarking studies.
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提高通才水平的集体共同进化策略
摘要在许多复杂的实际优化案例中,问题的主要特征往往是事先不知道的。因此,需要开发通用求解器,因为不可能总是为每个应用程序定制专门的方法。先前开发的多级选择遗传算法(MLSGA)由于其多样性优先的方法,已经在一系列问题上表现出良好的性能,这在进化算法中是罕见的。为了提高其性能的通用性,本文提出同时使用多个不同的进化策略,类似于算法选择,但子种群之间具有共同进化机制。这种独特的共同进化方法提供了亚种群之间不太规律的交流,集体之间而不是个人之间的竞争。这鼓励集体更加独立地行动,创造一个独特的次区域搜索,导致共同进化MLSGA(cMLSGA)的发展。为了测试这种方法,选择了九种遗传算法来生成cMLSGA的几种变体,该变体在个体水平上结合了这些方法。这些机制在100个不同的功能上进行了测试,并与9个最先进的竞争对手进行了对比,以评估每种方法的通用性。结果表明,所选择的共同进化方法的工作原理的多样性差异比它们的个体表现更重要。在测试的现有技术中,所提出的方法在不同的问题类型上具有最一致的性能,这使得算法更有可能在搜索空间知识有限的情况下解决复杂问题,但在更简单的基准测试研究中,更专业的解算器的表现更出色。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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