On the Performance of Genetic Operators and the Random Key Representation

Many evolutionary systems have been developed that solve various specific scheduling problems. In this work, one such permutation based system, which uses a linear GP type Genotype to Phenotype Mapping (GPM), known as the Random Key Genetic Algorithm is investigated. The role standard mutation plays in this representation is analysed formally and is shown to be extremely disruptive. To ensure small fixed sized changes in the phenotype a swap mutation operator is suggested for this representation. An empirical investigation reveals that swap mutation outperforms the standard mutation to solve a hard deceptive problem even without the use of crossover. Swap mutation is also used in conjunction with different crossover operators and significant boost has been observed in the performance especially in the case of headless chicken crossover that produced surprising results.