Title
Efficient preference learning algorithm for interactive evolutionary multi-objective optimization
Authors
[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2026
Published in
Article type
scientific article
Publication language
english
Keywords
Abstract
EN We propose a preference-learning algorithm tailored for interactive evolutionary multi-objective optimization. The method estimates the parameters of an assumed preference model from incomplete feedback provided by the decision maker (DM), addressing two challenges: (i) identifying compatible model instances even when preference information strongly constrains the parameter space, and (ii) generating a diverse, approximately uniform set of models to support robust decision making. These goals are achieved via an evolutionary process that iteratively refines a population of models using specialized operators. The algorithm prioritizes models that are both compatible with the elicited preferences and sufficiently dissimilar from their nearest neighbors, thereby promoting a well-distributed coverage of the feasible parameter space. To improve computational efficiency, we introduce a queue-based mechanism that directs the evolutionary process with minimal overhead, enhancing responsiveness for interactive use. We evaluate the proposed method in two complementary settings: first, as a standalone sampler, and second, embedded within an evolutionary multi-objective optimizer to demonstrate its utility for interactive decision support.
Date of online publication
10.12.2025
Pages (from - to)
102254-1 - 102254-20
Comments
Article Number: 102254
Ministry points / journal
140
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