A multi-objective optimization approach is o.en followed by an a posteriori decision-making process, during which the most appropriate solution of the Pareto set is selected by a professional in the .eld. Conventional visualization methods do not correct for Pareto fronts with irregularly-spaced solutions. However, achieving a uniform spread of solutions can make the decision-making process more intuitive when decision tools such as sliders, which represent the preference for each objective, are used. We propose a method that maps anm-dimensional Pareto front to an (m-1)-simplex and spreads out points to achieve a more uniform distribution of these points in the simplex while maintaining the local neighborhood structure of the solutions as much as possible. .is set of points can then more intuitively be navigated due to the more uniform distribution. We test our approach on a set of non-uniformly spaced 3D Pareto fronts of a real-world problem: deformable image registration of medical images. The results of these experiments are visualized as points in a triangle, showing that we indeed achieve a representation of the Pareto front with a near-uniform distribution of points where these are still positioned as expected, i.e., according to their quality in each of the objectives of interest.

Additional Metadata
Keywords GOMEA, Multi-objective optimization, Pareto front visualization, Real-valued optimization
Persistent URL dx.doi.org/10.1145/3067695.3082555
Project ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning
Conference Genetic and Evolutionary Computation Conference
Grant This work was funded by the The Netherlands Organisation for Scientific Research (NWO); grant id nwo/628.006.003 - ICT based Innovations in the Battle against Cancer – Next - Generation Patient -Tailored Brachytherapy Cancer Treatment Planning
Citation
Bouter, A, Pirpinia, K, Alderliesten, T, & Bosman, P.A.N. (2017). Spatial redistribution of irregularly-spaced Pareto fronts for more intuitive navigation and solution selection. In GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1697–1704). doi:10.1145/3067695.3082555