A new mutation operator for real coded genetic algorithms

In this paper, a new mutation operator called power mutation (PM) is introduced for real coded genetic algorithms (RCGA). The performance of PM is compared with two other existing real coded mutation operators taken from literature namely: non-uniform mutation (NUM) and Makinen, Periaux and Toivanen mutation (MPTM). Using the various combinations of two crossovers (Laplace crossover [Kusum Deep, Manoj Thakur, A new crossover operator for real coded genetic algorithms, Applied Mathematics and Computations, accepted for publication, doi:10.1016/j.amc.2006.10.047] and Heuristic crossover [Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, New York, 1992; A. H. Wright, Genetic algorithms for real parameter optimization, in: G. J. E. Rawlins (Ed.), Foundations of Genetic Algorithms I, Morgan Kaufmann, San Mateo, 1991, pp. 205-218]) and three mutation operators (the newly defined mutation in this paper, PM, NUM and MPTM) six generational real coded GAs are compared on a set of 20 benchmark global optimization test problems. Various performance criterion are used to judge the efficiency, accuracy and reliability of all the RCGAs. The results show that the RCGA using the proposed power mutation, when used in conjunction with the earlier defined Laplace crossover, outperforms all other GAs considered in this study. (C) 2007 Elsevier Inc. All rights reserved.