Choosing Representation, Mutation, and Crossover in Genetic Algorithms
Alexander Dockhorn, Simon M. Lucas
Abstract
This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.
Topics & Concepts
CrossoverComputer scienceRepresentation (politics)MutationGenetic algorithmAlgorithmGenetic representationTheoretical computer scienceArtificial intelligenceMachine learningGeneticsLawPoliticsGeneBiologyPolitical scienceMetaheuristic Optimization Algorithms ResearchEvolutionary Algorithms and Applications