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Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys

Duc-Cuong Dang, Anton V. Eremeev, Per Kristian Lehre

2021Proceedings of the Genetic and Evolutionary Computation Conference28 citationsDOIOpen Access PDF

Abstract

It is largely unknown how the runtime of evolutionary algorithms depends on fitness landscape characteristics for broad classes of problems. Runtime guarantees for complex and multi-modal problems where EAs are typically applied are rarely available.

Topics & Concepts

Fitness landscapeComputer scienceEvolutionary algorithmAlgorithmEvolutionary computationModalArtificial intelligenceDemographyChemistrySociologyPopulationPolymer chemistryEvolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchAdvanced Multi-Objective Optimization Algorithms
Non-elitist evolutionary algorithms excel in fitness landscapes with sparse deceptive regions and dense valleys | Litcius