Biased parameter adaptation in differential evolution : научное издание

Stanovov V.; Akhmedova S.; Semenkin E.

Дополнительные ссылки: eLibrary

База данных: Каталог библиотеки СФУ

Библиографическое описание: Biased parameter adaptation in differential evolution : научное издание / V. Stanovov, S. Akhmedova, E. Semenkin. - Текст : непосредственный // Information Sciences. - 2021. - Т. 566, № . - P. 215-238. - ISSN 0020-0255. - ISSN 1872-6291, DOI 10.1016/j.ins.2021.03.016.

Аннотация: The paper describes the problem of bias in parameter adaptation in Differential Evolution and other Evolutionary Algorithms. Based on the newly proposed Expected Fitness Improvement metric, the shift towards exploitation is demonstrated. The generalized Lehmer mean and Linear Bias Reduction are for the first time proposed to control the parameter adaptation bias for the fitness improvement based L-SHADE and distance based Db-L-SHADE algorithms. The experiments are performed on the benchmark functions of the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization. The influence of the modified scaling factor and crossover rate adaptation is evaluated using Friedman ranking procedure and Mann–Whitney statistical test. The usage of Lehmer mean and Linear Bias Reduction is shown to deliver statistically better results for high-dimensional functions and improve the exploration properties of the search algorithm in the long-term perspective. To support the statements made, the search process is additionally analyzed using diversity measures and cluster analysis techniques.

Год издания: 2021

Авторы: Stanovov V. , Akhmedova S. , Semenkin E.

Издательство: Elsevier Science Publishing Company, Inc.

Источник: Information Sciences

Выпуск: Т. 566

Номера страниц: 215-238

Держатель оригинала документа: Reshetnev Siberian State University of Science and Technology

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Biased parameter adaptation in differential evolution : научное издание
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