A Novel Essential Mutation Method for Evolutionary Algorithms

The mutation is one of the operators that is used by many Evolutionary Algorithms (EA) to diversify the population (solutions). It can enhance the algorithm exploration of the problem search space and improve the evolution process. This paper introduces a novel mutation technique that is based on a recently investigated mutation bias pattern in the Arabidopsis thaliana plant [1]. The proposed mutation technique is called an essential mutation. The proposed method uses the ϵ parameter to control the amount of distance we can be from the parent’s fitness. Three different configurations are studied and the best results are obtained when ϵ=0. It is compared against five well-known mutation techniques which are Boundary, Non-uniform, MPT, and Polynomial on standard benchmark functions. The obtained results show the superiority of the proposed essential mutation in terms of best solution and convergence speed in most of the test functions.