A Self-guided genetic algorithm with dominance properties for single machine scheduling problems

In this study we integrate a self-guided genetic algorithm with dominance properties (DPs) which is named DP-Self-guided GA. Self-guided GA is belonged to the category of evolutionary algorithms based on probabilistic models (EAPM) and it is effective and efficient in solving the scheduling problems. In order to further enhance the performance of this algorithm, it is thus integrated with DPs because DPs is a mathematical algorithm which is able to generate good solutions quickly. As a result, the solutions generated by DPs will be applied as the initial population of self-guided GA instead of using the randomly generated initial solutions. When we conducted an extensive experiments to validate DP-self-guided GA, it is statistically significant when we compared it with existing algorithms in the literature. As a result, the implication of this approach is a good heuristic which may further improve the performance of an EAPM algorithm.