Experimental Setup for GECCO 2024 Competition on Benchmarking Niching Methods for Multimodal Optimization

Genetic and Evolutionary Computation Conference Companion (GECCO Companion), pp. 5–6

Abstract

This work presents the experimental setup for the GECCO 2024 competition on benchmarking niching methods for multimodal optimization. It employs 16 recently developed scalable composite problems to evaluate different aspects of multimodal optimization methods. These test problems are available in Python and MATLAB.

Authors 5

1. Ali Ahrari

   UNSW Canberra

   Affiliation as printed

   University of New South Wales, Canberra, Australia

2. Jonathan Edward Fieldsend

   University of Exeter

   Affiliation as printed

   University of Exeter, Exeter, United Kingdom

3. Mike Preuß Aachen

   Leiden University

   Affiliation as printed

   Universiteit Leiden, Leiden, Netherlands

4. Xiaodong Li

   RMIT University

   Affiliation as printed

   RMIT University, Melbourne, Australia

5. Michael G. Epitropakis
   Affiliation as printed

   The Signal Group, Glyfada, Greece