Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandit
This work addresses the challenge of incorporating user preferences into evolutionary algorithms for practitioners in fields like bioinformatics, though it appears incremental as it builds on existing interactive MOEA frameworks.
The paper tackled the problem of multi-objective optimization by developing a method that uses direct human preferences instead of fitness functions, and demonstrated its application in protein structure prediction with experimental results.
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 feedback. Our proposed approach entails conducting direct preference learning facilitated by an active dueling bandit algorithm. The experimental phase is structured into three sessions. Firstly, we assess the performance of our active dueling bandit algorithm. Secondly, we implement our proposed method within the context of Multi-objective Evolutionary Algorithms (MOEAs). Finally, we deploy our method in a practical problem, specifically in protein structure prediction (PSP). This research presents a novel interactive preference-based MOEA framework that not only addresses the limitations of traditional techniques but also unveils new possibilities for optimization problems.