Different environmental conditions in genetic algorithm

Daekyung Lee and Beom Jun Kim

Physica A: Statistical Mechanics and its Applications, 2022, vol. 602, issue C

Abstract: We propose an extended genetic algorithm (GA) with different local environmental conditions. Genetic entities, or configurations, are put on nodes in a ring structure, and location-dependent environmental conditions are applied for each entity. Our GA is motivated by the geographic aspect of natural evolution: Geographic isolation reduces the diversity in a local group, but at the same time, can enhance intergroup diversity. Mating the genetic entities across different environments can make it possible to search for a broad area of the fitness landscape. We validate our extended GA for finding the ground state of the three-dimensional spin-glass system and find that the use of different environmental conditions makes it possible to find the lower-energy spin configurations at relatively shorter computation time. Our extension of GA belongs to a meta-optimization method and thus can be applied for a broad research area in which finding the optimal state in a shorter computation time is the key problem.

Keywords: Genetic algorithm; Meta-optimization; Spin-glass system; Natural evolution; Geographic separation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:602:y:2022:i:c:s0378437122004137

DOI: 10.1016/j.physa.2022.127604

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