Efficient Interactive Preference Learning in Evolutionary Algorithms: Active Dueling Bandits and Active Learning Integration
Primary Area: optimization
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Keywords: Preference learning, multi-objective optimization, interactive multi-objective optimization, active learning
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Abstract: Optimization problems find widespread use in both single-objective and multi-objective scenarios. In practical applications, users aspire for solutions that converge to the region of interest (ROI) along the Pareto front (PF). While the conventional approach involves approximating a fitness function or an objective function to reflect user preferences, this paper explores an alternative avenue. Specifically, we aim to discover a method that sidesteps the need for calculating the fitness function, relying solely on human guidance. Our proposed approach entails conducting a human-dominated search facilitated by an active dueling bandit algorithm.
The experimental phase is structured into three sessions. Firstly, we accsess the performance of our active dueling bandit algorithm. Secondly, we implement our proposed method within the context of multi-objective Evolutionary Algorithms (EAs). Finally, we deploy our method in a practical problem, specifically in protein structure prediction (PSP).
This research presents a novel interactive preference-based EA framework that not only addresses the limitations of traditional techniques but also unveils new possibilities for optimization problems.
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Submission Number: 3837
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