Self-adaptation Method for Evolutionary Algorithms Based on the Selection Operator

Genetic algorithms are a class of effective and popular black box optimization methods that are inspired by evolutionary processes in the nature. Genetic algorithms are useful in cases where nothing is known about the optimization object except the inputs and outputs. Such an algorithm iteratively searches for a solution in the solution space based on a predefined fitness function that allows comparing different solutions. If a researcher desires to use genetic algorithms, it becomes necessary to choose genetic operators and numerical parameters of the algorithm, the choice of which may be a difficult task. Self-adaptation methods that alter the behavior of the algorithm while it is running help to deal with the task of choosing the optimal settings of the algorithm. Such methods are called methods of self-adaptation of evolutionary algorithms and are usually divided into self-tuning, which performs the tuning of numerical parameters, and self-configuring, which makes the choice of genetic operators. In recent decades, various strategies for self-adaptation of evolutionary algorithms have been actively developed, including metaheuristic algorithms, as a result of which a researcher can obtain a specialized evolutionary algorithm that solves problems from a certain class better than conventional algorithms. However, even when using the metaheuristic approach, there is a need to choose genetic operators and numerical parameters of the algorithm. Therefore, the subject of the development of self-adaptive algorithms is one of the most relevant fields in the study of evolutionary algorithms. In this paper, a new approach to the adaptation of evolutionary algorithms based on the selection of genetic operators is proposed. The method is applied to the genetic algorithm and compared with the most popular SelfCGA self-configuring approach and shows an improvement in efficiency on both real and binary optimization problems.