Hybrid binary GA-EDA algorithms for complex "black-box" optimization problems : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Workshop on Mathematical Models and their Applications 2016, IWMMA 2016; Krasnoyarsk; Krasnoyarsk

Год издания: 2017

Идентификатор DOI: 10.1088/1757-899X/173/1/012019

Аннотация: Genetic Algorithms (GAs) have proved their efficiency solving many complex optimization problems. GAs can be also applied for "black-box" problems, because they realize the "blind" search and do not require any specific information about features of search space and objectives. It is clear that a GA uses the "Trial-and-Error" strategy to explorer search space, and collects some statistical information that is stored in the form of genes in the population. Estimation of Distribution Algorithms (EDA) have very similar realization as GAs, but use an explicit representation of search experience in the form of the statistical probabilities distribution. In this study we discus some approaches for improving the standard GA performance by combining the binary GA with EDA. Finally, a novel approach for the large-scale global optimization is proposed. The experimental results and comparison with some well-studied techniques are presented and discussed.

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Журнал: IOP Conference Series: Materials Science and Engineering

Выпуск журнала: 173

Номера страниц: 012019

Издатель: Institute of Physics Publishing


  • Sopov E. (Reshetnev Siberian State Aerospace University)

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