Soft Computing

, Volume 18, Issue 6, pp 1177–1188 | Cite as

A block based estimation of distribution algorithm using bivariate model for scheduling problems

  • Pei-Chann ChangEmail author
  • Meng-Hui Chen
Methodologies and Application


Recently, estimation of distribution algorithms (EDAs) have gradually attracted a lot of attention and have emerged as a prominent alternative to traditional evolutionary algorithms. In this paper, a block-based EDA using bivariate model is developed to solve combinatorial problems. Instead of generating a set of chromosomes, our approach generates a set of promising blocks using bivariate model and these blocks are reserved in an archive for future use. These blocks will be updated every other k generation. Then, two rules, i.e., AC1 and AC2, are developed to generate a new chromosome by combining the set of selected blocks and rest of genes. This block based approach is very efficient and effective when compared with the traditional EDAs. According to the experimental results, the block based EDA outperforms EDA, GA, ACO and other evolutionary approaches in solving benchmark permutation problems. The block based approach is a new concept and has a very promising result for other applications.


Combinatorial problems Estimation of distribution algorithms Bivariate probabilistic model Artificial chromosomes 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Information ManagementYuan Ze UniversityChung-LiTaiwan, ROC

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