New genetic algorithm based on dissimilarities and similarities

Abstract

Optimization is essential for nding suitable answers to real life problems. In particular, genetic (or more generally, evolutionary) algorithms can provide satisfactory approximate solutions to many problems to which exact analytcal results are not accessible. In this paper we present both theoretical and experimental results on a new genetic algorithm called Dissimilarity and Simlarity of Chromosomes (DSC). This methodology constructs new chromosomes starting with the pairs of existing ones by exploring their dissimilarities and similarities. To demonstrate the performance of the algorithm, it is run on 17 two-dimensional, one four-dimensional and two ten-dimensional optimization problems described in the literature, and compared with the well-known GA, CMA-ES and DE algorithms. The results of tests show the superiority of our strategy in the majority of cases.