A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem

Applied Sciences

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