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Evolutionary Algorithm with a Configurable Search Mechanism

Krystian Łapa, Krzysztof Cpałka, Łukasz Laskowski, Andrzej Cader, Zhigang Zeng

2020Journal of Artificial Intelligence and Soft Computing Research17 citationsDOIOpen Access PDF

Abstract

Abstract In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.

Topics & Concepts

Evolutionary algorithmComputer sciencePopulationEvolutionary programmingMechanism (biology)Domain (mathematical analysis)Cultural algorithmSearch algorithmEvolutionary computationAlgorithmMemetic algorithmENCODEOperator (biology)Artificial intelligenceMathematical optimizationMachine learningGenetic algorithmMathematicsPopulation-based incremental learningBiologyBiochemistryPhilosophyDemographyMathematical analysisRepressorEpistemologySociologyTranscription factorGeneMetaheuristic Optimization Algorithms ResearchEvolutionary Algorithms and ApplicationsAdvanced Multi-Objective Optimization Algorithms
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