Abstract
Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brockhoff, D., Auger, A., Hansen, N., Tušar, T.: Quantitative performance assessment of multiobjective optimizers: the average runtime attainment function. In: Trautmann, H., et al. (eds.) EMO 2017. LNCS, vol. 10173, pp. 103–119. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54157-0_8
Craven, M.J., Jimbo, H.C.: EA stability visualization: perturbations, metrics and performance. In: Proceedings of the Companion Publication of GECCO 2014, pp. 1083–1090 (2014)
Črepinšek, M., Liu, S.-H., Mernik, M.: Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput. Surv. (CSUR) 45(3), 1–33 (2013)
De Lorenzo, A., Medvet, E., Tušar, T., Bartoli, A.: An analysis of dimensionality reduction techniques for visualizing evolution. In: Proceedings of GECCO 2019, pp. 1864–1872 (2019)
Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2013)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002) (Cat. No. 02TH8600), vol. 1, pp. 825–830. IEEE (2002)
Fleischer, M.: The measure of Pareto optima applications to multi-objective metaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36970-8_37
He, Z., Yen, G.G.: Visualization and performance metric in many-objective optimization. IEEE Trans. Evol. Comput. 20(3), 386–402 (2015)
Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)
Kobayashi, Y., Okamoto, T., Koakutsu, S.: A Pareto optimal solution visualization method using SOM-NG with learning parameter optimization. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 004525–004531 (2016)
Torgerson, W.S.: Multidimensional scaling: I. theory and method. Psychometrika 17(4), 401–419 (1952). https://doi.org/10.1007/BF02288916
Tušar, T., Filipič, B.: Visualization of Pareto front approximations in evolutionary multiobjective optimization: a critical review and the prosection method. IEEE Trans. Evol. Comput. 19(2), 225–245 (2015)
Walker, D.J., Craven, M.J.: Toward the online visualisation of algorithm performance for parameter selection. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 547–560. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_38
Walkerm, D.J., Craven, M.J.: Identifying good algorithm parameters in evolutionary multi-and many-objective optimisation: a visualisation approach. Appl. Soft Comput. 88, 105902 (2020)
Walker, D.J., Everson, R.M., Fieldsend, J.E.: Visualizing mutually nondominating solution sets in many-objective optimization. IEEE Trans. Evol. Comput. 17(2), 165–184 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Walter, M.J., Walker, D.J., Craven, M.J. (2020). Visualising Evolution History in Multi- and Many-objective Optimisation. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-58115-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58114-5
Online ISBN: 978-3-030-58115-2
eBook Packages: Computer ScienceComputer Science (R0)