Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/5887
Title: Dual population-based incremental learning for problem optimization in dynamic environments
Authors: Yang, S
Yao, X
Keywords: Dynamic optimization;Population-based incremental learning;Dualism;Evolutionary algorithms
Issue Date: 2003
Publisher: Asia Pacific Symposium on Intelligent and Evolutionary Systems
Citation: 7th Asia Pacific Symposium on Intelligent and Evolutionary Systems: 49 - 56, 2003
Abstract: In recent years there is a growing interest in the research of evolutionary algorithms for dynamic optimization problems since real world problems are usually dynamic, which presents serious challenges to traditional evolutionary algorithms. In this paper, we investigate the application of Population-Based Incremental Learning (PBIL) algorithms, a class of evolutionary algorithms, for problem optimization under dynamic environments. Inspired by the complementarity mechanism in nature, we propose a Dual PBIL that operates on two probability vectors that are dual to each other with respect to the central point in the search space. Using a dynamic problem generating technique we generate a series of dynamic knapsack problems from a randomly generated stationary knapsack problem and carry out experimental study comparing the performance of investigated PBILs and one traditional genetic algorithm. Experimental results show that the introduction of dualism into PBIL improves its adaptability under dynamic environments, especially when the environment is subject to significant changes in the sense of genotype space.
Description: Copyright @ 2003 Asia Pacific Symposium on Intelligent and Evolutionary Systems
URI: http://bura.brunel.ac.uk/handle/2438/5887
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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