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Communication Dans Un Congrès Année : 2020

An ensemble indicator-based density estimator for evolutionary multi-objective optimization

Résumé

Ensemble learning is one of the most employed methods in machine learning. Its main ground is the construction of stronger mechanisms based on the combination of elementary ones. In this paper, we employ AdaBoost, which is one of the most well-known ensemble methods, to generate an ensemble indicator-based density estimator for multi-objective optimization. It combines the search properties of five density estimators, based on the hypervolume, R2, IGD+, ε+, and ∆p quality indicators. Through the multi-objective evolutionary search process, the proposed ensemble mechanism adapts itself using a learning process that takes the preferences of the underlying quality indicators into account. The proposed method gives rise to the ensemble indicator-based multi-objective evolutionary algorithm (EIB-MOEA) that shows a robust performance on different multi-objective optimization problems when compared with respect to several existing indicator-based multi-objective evolutionary algorithms.
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Dates et versions

hal-02920075 , version 1 (24-08-2020)

Identifiants

Citer

Jesús Guillermo Falcón-Cardona, Arnaud Liefooghe, Carlos A. Coello Coello. An ensemble indicator-based density estimator for evolutionary multi-objective optimization. PPSN 2020 - Sixteenth International Conference on Parallel Problem Solving from Nature, Sep 2020, Leiden, Netherlands. pp.201-214, ⟨10.1007/978-3-030-58115-2_14⟩. ⟨hal-02920075⟩
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