Genetic Algorithm Optimization: A Genome-Based Compound Crossover Strategy

Genetic algorithm (GA) is a widely used optimization method, and its performance is closely related to its crossover strategy. In this article, a novel mechanism named “genomic crossover” is designed, and by integrating it with existing operations, a compound crossover strategy is proposed. The gene grouping vector is derived through Tabu search. In addition to the conventional crossover operation, the genetic algorithm incorporates genome crossover with a certain probability. This enhances the feature recombination process, thereby accelerating overall optimization efficiency of the algorithm. The effectiveness of the strategy is validated by applying it to various function optimization processes and comparing it with different crossover algorithms. The results show that the performance of the compound crossover strategy is better than the traditional crossover strategy in complex problems, and the iterative process of the algorithm can be effectively optimized by this idea. This article not only validates the effectiveness of the compound strategy, but also provides a new perspective for the application of GA in complex problem-solving scenarios.