An Improved Directed Crossover Genetic Algorithm Based on Multilayer Mutation

Abstract

In order to solve the shortcomings of traditional genetic algorithms in image matching in terms of computational speed and matching accuracy, this paper proposes a directed crossover genetic matching algorithm (DCGA) based on multilayer variation. The algorithm differs from the traditional genetic algorithm (GA) in which the crossover strategy is improved and a multilayer adaptive variation operator is introduced. The crossover operation selects a certain proportion of spherical individuals from each generation as the evolutionary target, and the rest of the individuals evolve towards it in each dimension; the variation operation stratifies the population and adopts different adaptive variation methods for different layers. Avoiding the shortcomings of traditional genetic algorithms that tend to fall into local extremes, thus alleviating premature convergence, improves the search performance of the algorithm. The algorithm proposed in this paper is compared with the commonly used genetic algorithm by testing the effect of the function and tested practically in template matching. The experimental results show that the improved genetic algorithm has better convergence speed and search accuracy.