Fast non-elitist evolutionary algorithms with power-law ranking selection
Duc-Cuong Dang, Anton V. Eremeev, Per Kristian Lehre, Xiaoyu Qin
2022Proceedings of the Genetic and Evolutionary Computation Conference16 citationsDOIOpen Access PDF
Abstract
Theoretical evidence suggests that non-elitist evolutionary algorithms (EAs) with non-linear selection mechanisms can efficiently overcome broad classes of local optima where elitist EAs fail. However, the analysis assumes a weak selective pressure and mutation rates carefully chosen close to the "error threshold", above which they cease to be efficient. On problems easier for hill-climbing, the populations may slow down these algorithms, leading to worse runtime compared with variants of the elitist (1+1) EA.
Topics & Concepts
Evolutionary algorithmHill climbingSelection (genetic algorithm)Computer scienceEvolutionary computationLocal optimumRanking (information retrieval)Mathematical optimizationAlgorithmMutationMemetic algorithmPower (physics)Artificial intelligenceMathematicsGeneQuantum mechanicsPhysicsChemistryBiochemistryEvolutionary Algorithms and ApplicationsMetaheuristic Optimization Algorithms ResearchEvolution and Genetic Dynamics