Learning Adaptive Evolutionary Computation for Solving Multi-Objective Optimization Problems. (arXiv:2211.09719v1 [cs.NE])

Multi-objective evolutionary algorithms (MOEAs) are widely used to solve
multi-objective optimization problems. The algorithms rely on setting
appropriate parameters to find good solutions. However, this parameter tuning
could be very computationally expensive in solving non-trial (combinatorial)
optimization problems. This paper proposes a framework that integrates MOEAs
with adaptive parameter control using Deep Reinforcement Learning (DRL). The
DRL policy is trained to adaptively set the values that dictate the intensity
and probability of mutation for solutions during optimization. We test the …

arxiv computation optimization