Binary-like Real Coding Genetic Algorithm

Abstract

A new real coding genetic algorithm is proposed, which discretizes the continuous feasible region and then makes it continuous and complete by mutation operator and local search operator, thus achieving the uniformity of the discretization and continuity of the genetic algorithm. By comparison with binary genetic algorithm, differential evolution algorithm (DE), particle swarm optimization algorithm (PSO), simulated annealing algorithm (SA), and artificial bee colony algorithm (ABC), the results show that the proposed algorithm outperforms the others in all test functions. The algorithm is applied to the case of optimizing the weights of neural networks and excellent results are obtained, which validates the effectiveness of the algorithm.