ANNE - A new algorithm for evolution of artificial neural network classifier systems

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

6 Citations (Scopus)

Abstract

This paper introduces ANNE, a new algorithm for evolution of neural network classifiers. Different from standard divide and conquer approaches, the proposed algorithm evolves simultaneously the input feature vector, the network topology and the weights. The use of the embedded approach is also novel in an evolutionary feature selection paradigm. Tested on seven benchmark problems, ANNE creates compact solutions that achieve accurate and robust learning results. Significant reduction of the input features is obtained in most of the data sets. The performance of ANNE is compared to the performance of five control algorithms that combine different manual and automatic feature selection approaches with different structure design techniques. The tests show that ANNE performs concurrent feature selection and structure design with results that are equal or better than the best results obtained by algorithms specialised only on feature selection or neural network architecture design. Moreover, the proposed approach fully automates the neural network generation process, thus removing the need for time-consuming manual design.

Original languageEnglish
Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
Pages3294-3301
Number of pages8
Publication statusPublished - 2006
Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Conference

Conference2006 IEEE Congress on Evolutionary Computation, CEC 2006
Country/TerritoryCanada
CityVancouver, BC
Period16/07/0621/07/06

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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