Learning Value Functions in Interactive Evolutionary Multiobjective Optimization

Volume: 19, Issue: 1, Pages: 88 - 102
Published: Feb 1, 2015
Abstract
This paper proposes an interactive multiobjective evolutionary algorithm (MOEA) that attempts to learn a value function capturing the users’ true preferences. At regular intervals, the user is asked to rank a single pair of solutions. This information is used to update the algorithm’s internal value function model, and the model is used in subsequent generations to rank solutions incomparable according to dominance. This speeds up evolution...
Paper Details
Title
Learning Value Functions in Interactive Evolutionary Multiobjective Optimization
Published Date
Feb 1, 2015
Volume
19
Issue
1
Pages
88 - 102
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