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Reference Set Generator: A Method for Pareto Front Approximation and Reference Set Generation

Author

Listed:
  • Angel E. Rodriguez-Fernandez

    (Departmento de Computación, Centro de Investigación y de Estudios Avanzados del IPN, Mexico City 07360, Mexico)

  • Hao Wang

    (Leiden Institute of Advanced Computer Science and Applied Quantum Algorithms, Leiden University, 2311 EZ Leiden, The Netherlands)

  • Oliver Schütze

    (Departmento de Computación, Centro de Investigación y de Estudios Avanzados del IPN, Mexico City 07360, Mexico)

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.

Suggested Citation

  • Angel E. Rodriguez-Fernandez & Hao Wang & Oliver Schütze, 2025. "Reference Set Generator: A Method for Pareto Front Approximation and Reference Set Generation," Mathematics, MDPI, vol. 13(10), pages 1-28, May.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:10:p:1626-:d:1656574
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