Modified GA with the possibility of selecting a selection operator according to a set criterion
—Genetic algorithms are used to solve complex problems in various areas. Research related to genetic algorithms mainly focuses on its three operators: selection, crossover, and mutation. The need to improve the algorithm has led to the creation of different operators out of the three mentioned, many of which are adapted to specific problems. This paper deals with the most commonly used selection operators, and their influence on the efficiency and robustness of the genetic algorithm. The idea behind this paper is to combine selection operators inside the genetic algorithm during its execution to decrease the risk of selecting the inappropriate selection operator for the considered test function. Operators are combined so that preference in the current generation is given to the operator which produces the most suitable population according to the set criteria after crossover and mutation. The criteria used in this paper are the best average overall fitness of the population and the best individual fitness. This research has shown that the change in selection operators within genetic algorithm has positive effects on its functionality.