Unlearning Works Better Than You Think: Local Reinforcement-Based Selection of Auxiliary Objectives
Matthieu Lerasle, Abderrahim Bendahi, Adrien Fradin · Proceedings of the Genetic and Evolutionary Computation Conference · 2025
We introduce Local Reinforcement-Based Selection of Auxiliary Objectives (LRSAO), a novel approach that selects auxiliary objectives using reinforcement learning (RL) to support the optimization process of an evolutionary algorithm (EA) as in EA+RL framework and furthermore incorporates the ability to unlearn previously used objectives. By modifying the reward mechanism to penalize moves that do no increase the fitness value and relying on the local auxiliary objectives, LRSAO dynamically adapts its selection strategy to optimize performance according to the landscape and unlearn previous objectives when necessary.