This is the source code of the implementations of PNA-NSGA2[1] (denoted MMNSGAII on the plots),
MOBiDE[2] and NSGA2[3] used to perform the study published in [5] on all the function from the CEC2013 benchmark[4].
The implementation is done in JAVA using ECJ[6].

This implementation is provided as is, without any waranty.
No support is provided on the code nor will it be corrected.
It is here to reflect the implementation used to provide the
results presented in [5] and does not prentend to give a well
documented and correct version of the previously mentioned
algorithms. We did our best to implement them correctly and
hope we had though.

Copyright 2016 by Romaric Pighetti, CNRS, I3S
This work is licensed under the Academic Free License version 3.0.
Some of the work is derivated from Copyright 2006 by Sean Luke and George Mason University
licensed under the same license.

[1] 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.

[2] 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.

[3] 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. [Online]. Available:
    http://dx.doi.org/10.1109/4235.996017

[4] 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.
    [Online]. Available: http://goanna.cs.rmit.edu.au/~xiaodong/cec13-
    niching/competition/cec2013-niching-benchmark-tech-report.pdf

[5] 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.

[6] "A java-based evolutionary computation research system, ecj,"
    http://cs.gmu.edu/~eclab/projects/ecj/, accessed: 2016-09-19.
