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Co-evolution improves the efficiency of preference learning methods when the Decision Maker's aspirations develop over time


[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication


Chapter type

chapter in monograph / paper

Publication language


  • Evolutionary Multiple Objective Optimization
  • Preference Learning
  • Co-evolution
  • Pairwise Comparisons
  • Interactive Procedures

EN This paper's research scope is interactive evolutionary multiple objective optimization founded on the preference learning paradigm. It concerns a scenario in which the Decision Maker's (DM's) aspirations develop over time. In this view, the interactive method may be forced to occasionally re-learn the DM's value system and, thus, re-orient the search during optimization. In preliminary studies, we observed that although a satisfactory recommendation can ultimately be discovered, it is often attainable with more significant computational power. To resolve this issue, we propose a co-evolutionary method, evolving sub-populations that approximate the Pareto front or align with the DM's preferences. This diversifies the maintained solution set, which is useful when re-understanding the DM's aspirations during interactions. Also, it helps reallocate the preference-driven sub-population quickly, avoiding an extensive computational burden. We demonstrate the usefulness of such hybridization in a series of extensive experiments that involve different test problems, numbers of objectives, and behavioral models of the Decision Maker.

Date of online publication


Pages (from - to)

759 - 767





GECCO '23 : Proceedings of the Genetic and Evolutionary Computation Conference, July 15-19, 2023, Lisbon, Portugal

Presented on

GECCO '23 Genetic and Evolutionary Computation Conference, 15-19.07.2023, Lisbon, Portugal

Ministry points / chapter


Ministry points / conference (CORE)


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