Adaptive probabilities of crossover and mutation in genetic algorithms

In this paper we describe an efficient approach for solving the economic dispatch problem using Genetic Algorithms (GAs). We recommend the use of adaptive probabilities crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the convergence capacity of the GA. In the Adaptive Genetic Algorithm (AGA), the probabilities of crossover and mutation, pc and pm, are varied depending on the fitness values of the solutions. By using adaptively varying, pc and pm, we also provide a solution to the problem of deciding the optimal values of pc and pm, i.e., pc and pm need not be specified at all. We compare the performance of the AGA with that of the Standard GA (SGA) in optimizing the penalty function by using a sequential unconstrained minimization technique (SUMT). In this work, the AGA has been applied to a practical 14-bus system to show its feasibility and capabilities. The Numerical and graphical results show that the proposed approach is faster and more robust than the simple static Genetic Algorithm.