A new logistic distribution based crossover operator for real-coded genetic algorithm

Abstract

This paper proposed a new crossover operator called the Logistic crossover (LogX) which is used in conjunction with a well-known mutation operators Makinen, Periaux and Toivanen mutation (MPTM), non-uniform mutation (NUM) and power mutation (PM). The defined algorithm used the real encoded crossover and mutation operator. A set of 15 test problems have been taken from global optimization literature to test the performance of the proposed algorithm. Results are compared with some popular genetic algorithms (GAs) existing in the literature. The evaluation of performance of the proposed algorithm has been done by analysing the mean of the objective function values and by the Performance Index (PI). This comparative study shows that Logistic crossover operator (LogX) with three mutation operators outperform the other crossover operators.