Making Mutation Adaptive in Genetic Algorithm

Abstract

In classical Genetic Algorithm the nature of mutation is random so it only serves the purpose of adding diversity to the current generation and to avoid problems like premature convergence. In this paper it is shown that how mutation can be made adaptive so that when it occurs, it mutates the chromosome in a way so as to produce overall healthier chromosomes. The theory of adaptive mutation proposes that mutation may occur as a direct consequence of stress in the environment so that it can adapt to it. In this paper the mutation will follow up the theory of adaptive mutation and will try to mutate the chromosomes in a way so that it produces better results.

Keywords: Artificial Intelligence; Genetic Algorithm; Evolutionary Algorithm; Adaptive Mutation; Travelling Salesman Problem.