Adaptive Genetic Algorithm Based on Mutation and Crossover and Selection Probabilities

The Genetic Algorithm (GA) is an explore technique used to solve issues in many different applications. The genetic algorithm has some parameters, including crossover probability, selection mechanism, and mutation probability. In GA, parameter adaptation is an important research topic. This paper proposes a Probabilistic Adaptive Genetic Algorithm in which the mutation and crossover probabilities, as well as the selection mechanism are dynamically adapted throughout the running of the algorithm. A new set of rates is generated for the next iteration based on the differences between fitness values and individual, enhancing the searching global optimum exploitation. We have compared the proposed algorithm with some common and state-of-the-art adaptive strategies such as dynamic adaptive, dynamic deterministic, dynamic self-adaptive, and static on a set of several functions with varying degrees of complexity. Experimental results on several popular test functions have shown that the results of the proposed algorithm are significantly better than these methods on both convergence speed and the solutions' quality.The reason that the proposed method has better results than other methods is the adaptation of each parameter of the genetic algorithm.