New crossover operators using dominance and co-dominance principles for faster convergence of genetic algorithms

Normally, genetic algorithm (GA) does not guarantee global optimum for all optimization problems. Crossover operators play a crucial part in the convergence of GAs to a solution. Hence, if crossover is designed to pass on genes that highly contribute to the fitness of individuals, to subsequent generations, the convergence can be obtained faster while obtaining the best possible solution for the given initial population. In this paper, we propose two new crossover operators called the dominance and co-dominance crossover operators, based on the dominance and co-dominance principles of human genetics, respectively, to achieve this in case of applications using integer and real encoding. The dominance crossover operator is designed such that the child obtains a gene (feature) from a parent whose value for a particular gene (feature) is dominant than its value in the other parent. On the other hand, the co-dominance crossover operator is designed such that a child obtains two values for the same gene from both the parents in case both alleles (gene values) are equally dominant. These crossover operators were designed to get the optimal solution in less number of generations without sacrificing the performance of GA. The experiments conducted on test functions and two different problems, namely clustering (Reuters-21578 dataset) and learning to rank (LETOR dataset), emphasize that global optimum in fewer number of generations is obtained using our proposed crossover operators.