Benchmarking the Hill-Valley Evolutionary Algorithm for the GECCO 2018 Competition on Niching Methods Multimodal Optimization
This work provides incremental benchmarking for evolutionary algorithms in multimodal optimization, targeting researchers in optimization and niching methods.
The authors benchmarked the Hill-Valley Evolutionary Algorithm on the CEC2013 niching benchmark suite under GECCO 2018 competition rules, reporting results without problem-dependent tuning and discussing adjustments to the original method.
This report presents benchmarking results of the latest version of the Hill-Valley Evolutionary Algorithm (HillVallEA) on the CEC2013 niching benchmark suite. The benchmarking follows restrictions required by the GECCO 2018 competition on Niching methods for Multimodal Optimization. In particular, no problem dependent parameter tuning is performed. A number of adjustments have been made to original publication of HillVallEA that are discussed in this report.