Diverse evolutionary neural networks based on information theory

Kyung Joong Kim, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


There is no consensus on measuring distances between two different neural network architectures. Two folds of methods are used for that purpose: Structural and behavioral distance measures. In this paper, we focus on the later one that compares differences based on output responses given the same input. Usually neural network output can be interpreted as a probabilistic function given the input signals if it is normalized to 1. Information theoretic distance measures are widely used to measure distances between two probabilistic distributions. In the framework of evolving diverse neural networks, we adopted information-theoretic distance measures to improve its performance. Experimental results on UCI benchmark dataset show the promising possibility of the approach.

Original languageEnglish
Title of host publicationNeural Information Processing - 14th International Conference, ICONIP 2007, Revised Selected Papers
Number of pages10
EditionPART 2
Publication statusPublished - 2008
Event14th International Conference on Neural Information Processing, ICONIP 2007 - Kitakyushu, Japan
Duration: 2007 Nov 132007 Nov 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4985 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other14th International Conference on Neural Information Processing, ICONIP 2007

Bibliographical note

Funding Information:
This research was supported by Brain Science and Engineering Research Program sponsored by Korean Ministry of Commerce, Industry and Energy.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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