Conquering the needle-in-a-haystack: How correlated input variables beneficially alter the fitness landscape for neural networks

Stephen D. Turner, Marylyn D. Ritchie, William S. Bush

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

6 Scopus citations

Abstract

Evolutionary algorithms such as genetic programming and grammatical evolution have been used for simultaneously optimizing network architecture, variable selection, and weights for artificial neural networks. Using an evolutionary algorithm to perform variable selection while searching for non-linear interactions is akin to searching for a needle in a haystack. There is, however, a considerable amount of correlation among variables in biological datasets, such as in microarray or genetic studies. Using the XOR problem, we show that correlation between non-functional and functional variables alters the variable selection fitness landscape by broadening the fitness peak over a wider range of potential input variables. Furthermore, when sub-optimal weights are used, local optima in the variable selection fitness landscape appear centered on each of the two functional variables. These attributes of the fitness landscape may supply building blocks for evolutionary search procedures, and may provide a rationale for conducting a local search for variable selection.

Original languageEnglish (US)
Title of host publicationEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 7th European Conference, EvoBIO 2009, Proceedings
Pages80-91
Number of pages12
DOIs
StatePublished - Jul 23 2009
Event7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009 - Tubingen, Germany
Duration: Apr 15 2009Apr 17 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5483 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2009
CountryGermany
CityTubingen
Period4/15/094/17/09

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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