Applied Sciences | |
A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem | |
Maria Konieczka1  Alicja Poturała1  Jarosław Arabas1  Stanisław Kozdrowski1  | |
[1] Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland; | |
关键词: Population-Based Incremental Learning; Covariance Matrix Adaptation Evolution Strategy; Covariance Matrix Adaptation Population-Based Incremental Learning; knapsack problem; data correlation; | |
DOI : 10.3390/app11199136 | |
来源: DOAJ |
【 摘 要 】
The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algorithms was performed in the discrete domain of the solution space, where we used the well-known knapsack problem in a variety of data correlations. The results obtained show that the proposed CMA-PBIL algorithm can perform better than standard PBIL in some cases. Therefore, the proposed algorithm can be a reasonable alternative to the PBIL algorithm in the discrete space domain.
【 授权许可】
Unknown