Presentation information

General Session

General Session » GS-2 Machine learning

[1G3-GS-2b] 機械学習:最適化

Tue. Jun 8, 2021 3:20 PM - 5:00 PM Room G (GS room 2)

座長:高野 諒(立命館大学)

4:20 PM - 4:40 PM

[1G3-GS-2b-04] Approximating Weak Pareto Solution Sets for Multi-Objective Optimization Problems Using Deep Generative Models

〇Hinata Edo1,2, Naoki Hamada3,2, Kazuto Fukuchi1,2, Jun Sakuma1,2, Youhei Akimoto1,2 (1. University of Tsukuba, 2. RIKEN AIP, 3. KLab Inc.)

Keywords:Multiobjective Optimization, Deep Generative Models, weak pareto solution

The multi-objective evolutionary algorithm approximates the Pareto solution set by a finite number of solutions. In such an approach, as the number of objective functions increases, it is difficult to obtain the outline drawing of the Pareto solutions set. In this study, we propose a method to approximate the entire weak Pareto solution set by using a deep generative model. Focusing on the correspondence between the weight space of the Chebyshev scalarization approach and the set of weakly Pareto optimal solutions, we train a deep generative model that outputs the optimal solution of the Chebyshev scalarization function when a point on the standard unit is taken as the input and this is used as the weight vector. Experiments show that the proposed method obtains a more accurate Pareto solution set than some conventional methods when the number of objective functions is large.

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