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

Helong Li; Sam Kwong; Yi Hong

doi:10.1016/j.neucom.2010.06.032

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

Helong Li, Sam Kwong, Yi Hong

Department of Computer Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

6 Citations (Scopus)

Abstract

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. © 2011 Elsevier B.V.

Original language	English
Pages (from-to)	1868-1873
Journal	Neurocomputing
Volume	74
Issue number	11
DOIs	https://doi.org/10.1016/j.neucom.2010.06.032
Publication status	Published - May 2011

Research Keywords

Convergence
Global optimum
Population-Based Incremental Learning (PBIL)

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abstract = "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. {\textcopyright} 2011 Elsevier B.V.",

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AU - Kwong, Sam

AU - Hong, Yi

PY - 2011/5

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N2 - 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. © 2011 Elsevier B.V.

AB - 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. © 2011 Elsevier B.V.

KW - Convergence

KW - Global optimum

KW - Population-Based Incremental Learning (PBIL)

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DO - 10.1016/j.neucom.2010.06.032

M3 - RGC 21 - Publication in refereed journal

SN - 0925-2312

VL - 74

SP - 1868

EP - 1873

JO - Neurocomputing

JF - Neurocomputing

IS - 11

ER -

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

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Research Keywords

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