Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5987
Title: Experimental study on population-based incremental learning algorithms for dynamic optimization problems
Authors: Yang, S
Yao, X
Keywords: Population-based incremental learning;Dynamic optimization problem;Dual population-based incremental learning;Genetic algorithm;Central probability vector;Exclusive-or operator
Issue Date: 2005
Publisher: Springer
Citation: Soft Computing, 9(11): 815 - 834, Nov 2005
Abstract: Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which operates on two probability vectors that are dual to each other with respect to the central point in the genotype space. A diversity maintaining technique of combining the central probability vector into PBIL is also proposed to improve PBILs adaptability in dynamic environments. In this paper, a new dynamic problem generator that can create required dynamics from any binary-encoded stationary problem is also formalized. Using this generator, a series of dynamic problems were systematically constructed from several benchmark stationary problems and an experimental study was carried out to compare the performance of several PBIL algorithms and two variants of standard genetic algorithm. Based on the experimental results, we carried out algorithm performance analysis regarding the weakness and strength of studied PBIL algorithms and identified several potential improvements to PBIL for dynamic optimization problems.
Description: Copyright @ Springer-Verlag 2005.
URI: http://www.springerlink.com/content/u5k11nx786282q16/
http://bura.brunel.ac.uk/handle/2438/5987
DOI: http://dx.doi.org/10.1007/s00500-004-0422-3
ISSN: 1432-7643
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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