Self-adjusting offspring population sizes outperform fixed parameters on the cliff function
Mario Alejandro Hevia Fajardo, Dirk Sudholt
Abstract
In the discrete domain, self-adjusting parameters of evolutionary algorithms (EAs) has emerged as a fruitful research area with many runtime analyses showing that self-adjusting parameters can out-perform the best fixed parameters. Most existing runtime analyses focus on elitist EAs on simple problems, for which moderate performance gains were shown. Here we consider a much more challenging scenario: the multimodal function Cliff, defined as an example where a (1, λ) EA is effective, and for which the best known upper runtime bound for standard EAs is O(n25).
Topics & Concepts
Computer scienceFunction (biology)PopulationSimple (philosophy)Upper and lower boundsEvolutionary algorithmDomain (mathematical analysis)Focus (optics)CliffMathematical optimizationAlgorithmArtificial intelligenceMathematicsBiologyPaleontologyEpistemologyEvolutionary biologyPhilosophyMathematical analysisOpticsPhysicsSociologyDemographyMetaheuristic Optimization Algorithms ResearchEvolutionary Algorithms and ApplicationsAdvanced Multi-Objective Optimization Algorithms