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Direct Preference-Based Evolutionary Multi-Objective Optimization with Dueling Bandit

arXiv 2311.14003 ↗First posted 23 Nov 2023cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 65 references that are in the map. First posted on arXiv 23 Nov 2023.

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17 of 65 references.

References · 17

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  2. 2020A Survey of Deep Active Learningcited by 554
  3. 2018Preference-based Online Learning with Dueling Bandits: A Surveycited by 102
  4. 2023Inverse Preference Learning: Preference-based RL without a Reward Functioncited by 68
  5. 2015Regret Lower Bound and Optimal Algorithm in Dueling Bandit Problemcited by 52
  6. 2016Double Thompson Sampling for Dueling Banditscited by 42
  7. 2015Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Banditscited by 29
  8. 2023qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimizationcited by 23
  9. 2020Preferential Batch Bayesian Optimizationcited by 19
  10. 2018Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Makingcited by 18
  11. 2022Human Preferences as Dueling Banditscited by 14
  12. 2023Active Reward Learning from Online Preferencescited by 13
5 more
  1. 2019Does Preference Always Help? A Holistic Study on Preference-Based Evolutionary Multi-Objective Optimisation Using Reference Pointscited by 7
  2. 2013Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lenscited by 6
  3. 2018Interactive Decomposition Multi-Objective Optimization via Progressively Learned Value Functionscited by 5
  4. 2022Interactive Evolutionary Multi-Objective Optimization via Learning-to-Rankcited by 3
  5. 2020Learning to Actively Learn: A Robust Approachcited by 2

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