MODIFICATION OF GENETIC ALGORITHM WITH ADAPTIVE CROSSOVER SWITCHING

The aim of this work is to develop a modification of the adaptive genetic algorithm based on switching crossover in accordance with the degree of elitism of individuals in the population. Despite the enormous amount of research done in the field of evolutionary calculus in recent years, algorithms of this class today have a high prospect of modification. The main aim of research is carried out in order to improve the convergence rate of algorithms (to obtain high-performance optimization methods) and increase the accuracy of the solutions obtained. In the article, for the adaptive tuning of the crossover operator, the concepts of discrete and continuous degree of elitism of individuals are used. In addition, an elitism score is used to adjust the probability of a mutation. This modification has a serious advantage superiority in test problems which are traditionally used to analyze the efficiency of genetic algorithms. The test set used was a quadratic function with three variables, a Rosenbrock function, a step function, a complex fourth-order function with noise, and the Sheckel function. The results of comparing classical genetic algorithms with algorithms using the considered crossover and mutation tuning strategies are presented. An analysis of the results of a computational experiment is presented.

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