Work

Level-Based Analysis of Genetic Algorithms for Combinatorial Optimization

Year: 2015

Type: preprint

Abstract: The paper is devoted to upper bounds on run-time of Non-Elitist Genetic Algorithms until some target subset of solutions is visited for the first time. In particular, we consider the sets of optimal solutions and the sets of local optima as the target subsets. Previously known upper bounds are improved by means of drift analysis. Finally, we propose conditions ensuring that a Non-Elitist Genetic Algorithm efficiently finds approximate solutions with constant approximation ratio on the class of combinatorial optimization problems with guaranteed local optima (GLO).

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1512.02047

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

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Russian Foundation for Basic Research, Engineering and Physical Sciences Research Council

Awards: G1956160721, G3207356259, G393177772, The LANCS (Lancaster, Nottingham, Cardiff and Southampton) Initiative in Foundational Operational Research: Building Theory for Practice, Speed of Adaptation in Population Genetics and Evolutionary Computation, G5702163051, G5878972985

OpenAlex ID: W2192308248

Cites: 22

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Related to: 20