A modified real coded genetic algorithm for constrained optimization

Abstract

The performance of a genetic algorithm (GA) largely depends upon crossover and mutation operators. Deep and Thakur (2007) [14,15] proposed a real coded genetic algorithm (RCGA) incorporating Laplace crossover (LX) and power mutation (PM) operator and shown that the resulting GA (named LX–PM) outperforms many existing RCGAs on a large set of scalable test problems of varying difficulty level. In this paper, LX–PM is modified by improving the LX operator. The modified LX operator, named as bounded exponential crossover (BEX) operator, always creates offspring within the variable bounds. A new RCGA (named BEX–PM) incorporating BEX and PM operator is proposed. The performance of the modified GA is tested against the original algorithm LX–PM and three other popular constrained optimization algorithms (HX-NUM, HX-MPTM and SBX-POL) over a test suite containing twenty five constrained optimization problems collected from global optimization literature. The performance of all RCGAs and quality of solution obtained is compared on the basis of standard criteria used in GA literature. The comparative study shows that BEX–PM performs significantly better than the original algorithm LX–PM and outperforms all RCGAs considered in this study.