Reinforcement Learning Based Dynamic Selection of Auxiliary Objectives with Preserving of the Best Found Solution
This work addresses optimization efficiency for evolutionary algorithms, but it is incremental as it modifies an existing EA+RL method.
The paper tackles the problem of improving single-objective optimization efficiency by dynamically selecting auxiliary objectives using reinforcement learning, preventing loss of the best solution, and shows that the proposed modification outperforms the EA+RL method on all problem instances and the single-objective approach on most instances.
Efficiency of single-objective optimization can be improved by introducing some auxiliary objectives. Ideally, auxiliary objectives should be helpful. However, in practice, objectives may be efficient on some optimization stages but obstructive on others. In this paper we propose a modification of the EA+RL method which dynamically selects optimized objectives using reinforcement learning. The proposed modification prevents from losing the best found solution. We analysed the proposed modification and compared it with the EA+RL method and Random Local Search on XdivK, Generalized OneMax and LeadingOnes problems. The proposed modification outperforms the EA+RL method on all problem instances. It also outperforms the single objective approach on the most problem instances. We also provide detailed analysis of how different components of the considered algorithms influence efficiency of optimization. In addition, we present theoretical analysis of the proposed modification on the XdivK problem.