Benchmarking HillVallEA for the GECCO 2019 Competition on Multimodal Optimization

2019/07/25 by S. C. Maree, Maree, S. C., Tanja Alderliesten +3
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.1907.10988

openalex publication_date 2019/07/25 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28

Abstract

This report presents benchmarking results of the Hill-Valley Evolutionary Algorithm version 2019 (HillVallEA19) on the CEC2013 niching benchmark suite under the restrictions of the GECCO 2019 niching competition on multimodal optimization. Performance is compared to algorithms that participated in previous editions of the niching competition.

Citations

• Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization

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