Permutation free encoding technique for evolving neural networks

Anupam Das, Md Shohrab Hossain, Saeed Muhammad Abdullah, Rashed Ul Islam

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

2 Scopus citations


This paper presents a new evolutionary system using genetic algorithm for evolving artificial neural networks (ANNs). The proposed algorithm is "Permutation free Encoding Technique for Evolving Neural Networks"(PETENN) that uses a novel encoding scheme for representing ANNs. Existing genetic algorithms (GAs) for evolving ANNs suffer from the permutation problem, resulting from the recombination operator. Evolutionary Programming (EP) does not use recombination operator entirely. But the proposed encoding scheme avoids permutation problem by applying a sorting technique. PETENN uses two types of recombination operators that ensure automatic addition or deletion of nodes or links during the crossover process. The evolutionary system has been implemented and applied to a number of benchmark problems in machine learning and neural networks. The experimental results show that the system can dynamically evolve ANN architectures, showing competitiveness and, in some cases, superiority in performance.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Networks - ISNN 2008 - 5th International Symposium on Neural Networks, ISNN 2008, Proceedings
PublisherSpringer Verlag
Number of pages11
EditionPART 1
ISBN (Print)3540877312, 9783540877318
StatePublished - 2008
Event5th International Symposium on Neural Networks, ISNN 2008 - Beijing, China
Duration: Sep 24 2008Sep 28 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5263 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th International Symposium on Neural Networks, ISNN 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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