A Gender Genetic Algorithm and Its Comparison with Conventional Genetic Algorithm

Авторы: Kupriyanov Gavriil, Isaev Igor, Dolenko Sergey
Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research VI
Серия: Studies in Computational Intelligence
Том: 1064
Год издания: 2023
Издательство: Springer International Publishing AG
Местоположение издательства: Cham, Switzerland
Первая страница: 158
Последняя страница: 166
DOI: 10.1007/978-3-031-19032-2_16
Аннотация: This study presents a new variety of gender genetic algorithm (GGA). Using the example of five test optimization problems, its superiority over conventional genetic algorithm (GA) with an elitism operator is shown. The analysis of the novel GGA on multi-extreme optimization functions confirms the effectiveness of the idea of gender separation of the population. Based on the results obtained from experimental data and on their analysis, a more advanced modification of GGA is proposed, using determination of the offspring’s gender based on the combination of parental chromosomes in it.
Добавил в систему: Куприянов Гавриил Александрович

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