RSG, a Method for Pareto Front Approximation and Reference Set Generation

Preprints.org

Abstract

In this paper, we address the problem of obtaining bias-free and complete finite size approximations of the solution sets (Pareto fronts) of multi-objective optimization problems (MOPs). Such approximations are, in particular, required for the fair usage of distance-based performance indicators, which are frequently used in evolutionary multi-objective optimization (EMO). If the Pareto front approximations are biased or incomplete, the use of these performance indicators can lead to misleading or false information. To address this issue, we propose the Reference Set Generator (RSG), which can, in principle, be applied to Pareto fronts of any shape and dimension. We finally demonstrate the strength of the novel approach on several benchmark problems.

Authors 3

1. Angel E. Rodríguez-Fernandez

   Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional · Instituto Politécnico Nacional

   Affiliation as printed

   Centro de Investigación y de Estudios Avanzados del IPN , Departmento de Computación , Mexico City , Mexico

2. Hao Wang Aachen Leiden Institute of Advanced Computer Science and Applied Quantum Algorithms

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science and Applied Quantum Algorithms , Leiden University , Leiden , The Netherlands

3. Oliver Schütze

   Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional · Instituto Politécnico Nacional

   Affiliation as printed

   Centro de Investigación y de Estudios Avanzados del IPN , Departmento de Computación , Mexico City , Mexico