The convergence analysis and specification of the Population-Based Incremental Learning algorithm

In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration p ( 0 ) is fixed and the learning rate α is close to zero. The convergence in probability of PBIL is confirmed by the experiment...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 74; no. 11; pp. 1868 - 1873
Main Authors: Li, Helong, Kwong, Sam, Hong, Yi
Format: Journal Article
Language:English
Published: Elsevier B.V 01.05.2011
Subjects:
Convergence
Global optimum
Population-Based Incremental Learning (PBIL)
Global optimum
Population-Based Incremental Learning (PBIL)
Convergence
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:In this paper, we investigate the global convergence properties in probability of the Population-Based Incremental Learning (PBIL) algorithm when the initial configuration p ( 0 ) is fixed and the learning rate α is close to zero. The convergence in probability of PBIL is confirmed by the experimental results. This paper presents a meaningful discussion on how to establish a unified convergence theory of PBIL that is not affected by the population and the selected individuals.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2010.06.032