Out-of-the-box parameter control for evolutionary and swarm-based algorithms with distributed reinforcement learning

Parameter control methods for metaheuristics with reinforcement learning put forward so far usually present the following shortcomings: (1) Their training processes are usually highly time-consuming and they are not able to benefit from parallel or distributed platforms; (2) they are usually sensitive to their hyperparameters, which means that the quality of the final results is heavily dependent on their values; (3) and limited benchmarks have been used to assess their generality. This paper addresses these issues by proposing a methodology for training out-of-the-box parameter control policies for mono-objective non-niching evolutionary and swarm-based algorithms using distributed reinforcement learning with population-based training. The proposed methodology is suitable to be used in any mono-objective optimization problem and for any mono-objective and non-niching Evolutionary and swarm-based algorithm. The results in this paper achieved through extensive experiments show that the proposed method satisfactorily improves all the aforementioned issues, overcoming constant, random and human-designed policies in several different scenarios.