A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem
This work addresses parameter control in optimization algorithms, offering a new perspective for researchers in evolutionary computation and reinforcement learning, though it appears incremental as it applies known RL techniques to a specific EA problem.
The paper tackled the problem of optimally controlling mutation probabilities in a (1+1) evolutionary algorithm on the OneMax function using reinforcement learning, resulting in a method that can incorporate prior knowledge and does not require exact transition probabilities.
We study how Reinforcement Learning can be employed to optimally control parameters in evolutionary algorithms. We control the mutation probability of a (1+1) evolutionary algorithm on the OneMax function. This problem is modeled as a Markov Decision Process and solved with Value Iteration via the known transition probabilities. It is then solved via Q-Learning, a Reinforcement Learning algorithm, where the exact transition probabilities are not needed. This approach also allows previous expert or empirical knowledge to be included into learning. It opens new perspectives, both formally and computationally, for the problem of parameter control in optimization.