Hybridizing A Genetic Algorithm With Reinforcement Learning for Automated Design of Genetic Algorithms

The automated design of optimization techniques holds great promise for advancing state-of-the-art optimization techniques and it has already taken over the manual design by human experts in some problems. Genetic algorithms are one of the key approaches for tackling the automated design problem. Unfortunately, these algorithms may take several hours to run as the fitness evaluation involves solving some benchmark instances to determine the quality of a candidate configuration. In this paper, we hybridize a meta-genetic algorithm with reinforcement learning to automatically design genetic algorithms for the two-dimensional bin packing problem. The task of the meta-genetic algorithm is to search the configuration space of genetic algorithms and the task of reinforcement learning is to decide whether to evaluate a candidate configuration or not. Therefore, avoiding wasting the computational budget on poor configurations. The proposed hybrid and the meta-genetic algorithm without reinforcement learning produce solvers for the two-dimensional bin packing problem that are competitive with the state-of-the-art algorithms. However, the proposed hybrid consumes about 25% of the computational effort required by the meta-genetic algorithm without reinforcement learning.