Parameter Control Framework for Multiobjective Evolutionary Computation Based on Deep Reinforcement Learning
Tianwei Zhou,
Wenwen Zhang,
Ben Niu
et al.
Abstract:To address the challenge of parameter adjustment in complex environments, this paper introduces a transfer learning-based parameter control framework via deep reinforcement learning for multiobjective evolutionary algorithms (MOEAs). To avoid the requirement for accurate Pareto front information, this framework is proposed with comprehensive global-state information, including basic problem features, the relative position of individuals, the distribution of fitness value, and the grid-IGD. Building on this fra… Show more
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