Menu
News
- 2025, March : PhD Research Proposal on Mixed-Integer optimisation by combining Reinforcement Learning and Artificial Evolution
Sidebar
soft_multimodal
Comparative Study of Recent Multimodal Evolutionary Algorithms
This page contains complimentary material for the paper Comparative Study of Recent Multimodal Evolutionary Algorithms 1). This includes the implementation of PNA-NSGA2 2) (denoted MMNSGAII on the plots), MOBiDE 3) and NSGA-II 4) used to obtain the presented results, as well as results obtained for all the functions from the CEC2013 benchmark 5). Implementations were done in JAVA using ECJ library 6).
All the results are computed with a precision of 1e-3.
1)
Pighetti, R., Pallez, D., & Precioso, F. (2015, December). “Comparative Study of Recent Multimodal Evolutionary Algorithms”. In Computational Intelligence, 2015 IEEE Symposium Series on (pp. 837-844). IEEE. Available here
2)
S. Bandaru and K. Deb,“A parameterless-niching-assisted bi-objective approach to multimodal optimization”, in Evolutionary Computation (CEC), 2013 IEEE Congress on, June 2013, pp. 95–102. Available here
3)
A. Basak, S. Das, and K. Tan, “Multimodal optimization using a biobjective differential evolution algorithm enhanced with mean distance based selection”, Evolutionary Computation, IEEE Transactions on, vol. 17, no. 5, pp. 666–685, Oct 2013. Available here
4)
K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II”, Trans. Evol. Comp, vol. 6, no. 2, pp. 182–197, Apr. 2002. Available here
5)
X. Li, A. Engelbrecht, and M. G. Epitropakis, “Benchmark functions for cec’2013 special session and competition on niching methods for multimodal function optimization”, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, Tech. Rep., 2013. Available here
6)
“A java-based evolutionary computation research system, ecj”, Available here, accessed: 2016-09-19.
soft_multimodal.txt · Last modified: 2016/09/21 09:19 by Denis Pallez
Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 4.0 International
