Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: IEEE Symposium Series on Computational Intelligence, SSCI 2020; Virtual, Canberra, ACT; Virtual, Canberra, ACT
Год издания: 2020
Идентификатор DOI: 10.1109/SSCI47803.2020.9308467
Ключевые слова: differential evolution, parameter adaptation, population diversity, success-history adaptation
Аннотация: In this paper the expected fitness improvement metric is proposed to visualize the parameter search space in Differential Evolution. The expected fitness improvement is estimated at every generation of the algorithm and plotted in a heatmap profile. The spread of promising scaling factor values is analyzed for the SHADE and jDE algПоказать полностьюorithms with two different mutation strategies. In addition, the distance between the individuals in the population is considered, and the connection between distance and scaling factor values is observed. The performed experiments reveal important properties of Differential Evolution mutation operators, as well as widely used parameter adaptation techniques.
Журнал: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
Номера страниц: 321-328
Издатель: Institute of Electrical and Electronics Engineers Inc.