Improving genetic algorithm using arc consistency technic

We studied in this article a topic that focused on two areas of research: Constraint Satisfaction Problems (CSP) and genetic algorithms. The problem is that this type of algorithm is recognized to be greedy in terms of CPU time. To solve this problem, we tried to integrate the arc consistency (AC) at the initial population in a way that it would be the result of this filtering. First, we generated the genetic algorithm without integrating the arc consistency. Then, we considered that each chromosome is a CSP, each gene is a variable of the problem and each allele represents the taken value. We randomly generated the CSP to obtain the inconsistent values of each pair of variables. To remove these values, we used the technique of arc consistency as a technique for solving this type of problem, that means we have worked to eliminate from each variables domain the values which violate the constraint specific and make the CSP inconsistent. The aim of this work is to reduce performance in terms of execution time of the genetic algorithm.