Learning and evolution by minimization of mutual information

Yong LIU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

11 Citations (Scopus)

Abstract

Based on negative correlation learning [1] and evolutionary learning, evolutionary ensembles with negative correlation learning (EENCL) was proposed for learning and designing of neural network ensembles [2]. The idea of EENCL is to regard the population of neural networks as an ensemble, and the evolutionary process as the design of neural network ensembles. EENCL used a fitness sharing based on the covering set. Such fitness sharing did not make accurate measurement on the similarity in the population. In this paper, a fitness sharing scheme based on mutual information is introduced in EENCL to evolve a diverse and cooperative population. The effectiveness of such evolutionary learning approach was tested on two real-world problems. This paper has also analyzed negative correlation learning in terms of mutual information on a regression task in the different noise conditions. © Springer-Verlag Berlin Heidelberg 2002.
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature : PPSN VII 7th International Conference, Granada, Spain, September 7-11, 2002, Proceedings
EditorsJuan Julián MERELO GUERVÓS, Panagiotis ADAMIDIS, Hans-Georg BEYER, Hans-Paul SCHWEFEL, José-Luis FERNÁNDEZ-VILLACAÑAS
PublisherSpringer Berlin Heidelberg
Pages495-504
Number of pages10
Volume2439
ISBN (Electronic)9783540457121
ISBN (Print)9783540441397
DOIs
Publication statusPublished - 2002
Externally publishedYes
EventPPSN 2002: International Conference on Parallel Problem Solving from Nature - Granada, Spain
Duration: 7 Sept 200211 Sept 2002

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Berlin, Heidelberg
Volume2439
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferencePPSN 2002: International Conference on Parallel Problem Solving from Nature
Country/TerritorySpain
CityGranada
Period7/09/0211/09/02

Keywords

  • Neural Network
  • Mutual Information
  • Hide Node
  • Individual Network
  • Evolutionary Learning

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