Hybrid binary GA-EDA algorithms for complex "black-box" optimization problems

5th International Workshop on Mathematical Models and their Applications 2016

Sopov, E.^1

Reshetnev Siberian State Aerospace University, 31 Krasnoyarskiy Rabochiy Ave., Krasnoyarsk

660037, Russia^1

关键词: Complex optimization problems;    Estimation of distribution algorithm (EDA);    Explicit representation;    Genetic algorithm (GAs);    Large scale global optimizations;    Optimization problems;    Statistical information;    Statistical probability;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/173/1/012019/pdf
DOI  :  10.1088/1757-899X/173/1/012019

来源: IOP

【 摘 要 】

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.