A Crossover Operator Based On Building Blocks Preservation

In this study, we propose a novel crossover operator for solving optimization problems in genetic algorithms. Our method preserves existing building blocks in the parent chromosomes, which improves the convergence rate and the quality of the results. We compare the performance of our proposed method with three other crossover operators, including one-point, n-point, and uniform crossovers. We use a set of X test problems from the global optimization literature to evaluate the performance of these four genetic algorithms. To assess the effectiveness of our proposed method, we conduct two types of analysis, including a comparison of the convergence curves and an evaluation of the quality of the results obtained.