Conquering the Needle-in-a-Haystack: How Correlated Input Variables Beneficially Alter the Fitness Landscape for Neural Networks

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@InProceedings{turner:2009:evobio,
  author =       "Stephen D. Turner and Marylyn D. Ritchie and 
                 William S. Bush",
  title =        "Conquering the Needle-in-a-Haystack: How Correlated
                 Input Variables Beneficially Alter the Fitness
                 Landscape for Neural Networks",
  booktitle =    "EvoBIO 2009, Proceedings of the 7th European
                 Conference on Evolutionary Computation, Machine
                 Learning and Data Mining in Bioinformatics",
  year =         "2009",
  editor =       "Clara Pizzuti and Marylyn Ritchie",
  volume =       "5483",
  series =       "Lecture Notes in Computer Science",
  pages =        "80--91",
  address =      "Tuebingen, Germany",
  publisher_address = "Berlin Heidelberg New York",
  month =        apr # " 15-17",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, NiH",
  isbn13 =       "978-3-642-01183-2",
  DOI =          "doi:10.1007/978-3-642-01184-9_8",
  size =         "12 pages",
  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 suboptimal 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.",
  notes =        "EvoBIO2009",
}

Genetic Programming entries for Stephen D Turner Marylyn D Ritchie William S Bush

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