Postprocessed data

Below, we display the postprocessed (html) output of the COCO platform (Hansen et al. 2021) when comparing the algorithms COMO-CMA-ES (COMO-100) (Dufossé and Touré 2019; Touré et al. 2019) SMS-EMOA-DE (Auger et al. 2016a; Beume et al. 2007), and NSGA-II-Matlab (Auger et al. 2016b; Deb et al. 2002) on the bbob-biobj test suite.

Click here to view the output as separate web pages.

The plots are retrieved from the bbob-biobj-plots GitHub repository.

References

Auger, A., Brockhoff, D., Hansen, N., Tušar, D., Tušar, T., and Wagner, T. (2016b), “Benchmarking MATLAB’s gamultiobj (NSGA-II) on the bi-objective BBOB-2016 test suite,” in Companion proceedings of the genetic and evolutionary computation conference, GECCO 2019, ACM, pp. 1233–1239. https://doi.org/10.1145/2908961.2931706.

Auger, A., Brockhoff, D., Hansen, N., Tušar, D., Tušar, T., and Wagner, T. (2016a), “The impact of variation operators on the performance of SMS-EMOA on the bi-objective BBOB-2016 test suite,” in Companion proceedings of the genetic and evolutionary computation conference, GECCO 2019, ACM, pp. 1225–1232. https://doi.org/10.1145/2908961.2931705.

Beume, N., Naujoks, B., and Emmerich, M. T. M. (2007), “SMS-EMOA: Multiobjective selection based on dominated hypervolume,” European Journal of Operational Research, 181, 1653–1669. https://doi.org/10.1016/J.EJOR.2006.08.008.

Deb, K., Agrawal, S., Pratap, A., and Meyarivan, T. (2002), “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, 6, 182–197. https://doi.org/10.1109/4235.996017.

Dufossé, P., and Touré, C. (2019), “Benchmarking MO-CMA-ES and COMO-CMA-ES on the bi-objective bbob-biobj testbed,” in Companion proceedings of the genetic and evolutionary computation conference, GECCO 2019, ACM, pp. 1920–1927. https://doi.org/10.1145/3319619.3326892.

Hansen, N., Auger, A., Ros, R., Mersmann, O., Tušar, T., and Brockhoff, D. (2021), “COCO: A platform for comparing continuous optimizers in a black-box setting,” Optimization Methods and Software, 36, 114–144. https://doi.org/10.1080/10556788.2020.1808977.

Touré, C., Hansen, N., Auger, A., and Brockhoff, D. (2019), “Uncrowded hypervolume improvement: COMO-CMA-ES and the Sofomore framework,” in Proceedings of the genetic and evolutionary computation conference, GECCO 2019, ACM, pp. 638–646. https://doi.org/10.1145/3321707.3321852.