  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.
