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Found 2 papers, 1 papers with code
Constrained Multi-objective Optimization with Deep Reinforcement Learning Assisted Operator Selection
By using a Q-Network to learn a policy to estimate the Q-values of all actions, the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.
MToP: A MATLAB Optimization Platform for Evolutionary Multitasking
Despite the abundance of multitask evolutionary algorithms (MTEAs) proposed for multitask optimization (MTO), there remains a comprehensive software platform to help researchers evaluate MTEA performance on benchmark MTO problems as well as explore real-world applications.
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