Dynamical transitions in the evolution of learning algorithms by selection

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@TechReport{neirotti:2002:0209048,
  author =       "Juan Pablo Neirotti and Nestor Caticha",
  title =        "Dynamical transitions in the evolution of learning
                 algorithms by selection",
  institution =  "Departamento de Fisica Geral, Instituto de Fisica,
                 Universidade de Sao Paulo, Brazil",
  year =         "2002",
  number =       "0209048",
  keywords =     "genetic algorithms, genetic programming, Biological
                 Physics, Disordered Systems and Neural Networks",
  URL =          "http://arxiv.org/PS_cache/physics/pdf/0209/0209048.pdf",
  URL =          "http://arxiv.org/abs/physics/0209048",
  abstract =     "We study the evolution of artificial learning systems
                 by means of selection. Genetic programming is used to
                 generate a sequence of populations of algorithms which
                 can be used by neural networks for supervised learning
                 of a rule that generates examples. In opposition to
                 concentrating on final results, which would be the
                 natural aim while designing good learning algorithms,
                 we study the evolution process and pay particular
                 attention to the temporal order of appearance of
                 functional structures responsible for the improvements
                 in the learning process, as measured by the
                 generalisation capabilities of the resulting
                 algorithms. The effect of such appearances can be
                 described as dynamical phase transitions. The concepts
                 of phenotypic and genotypic entropies, which serve to
                 describe the distribution of fitness in the population
                 and the distribution of symbols respectively, are used
                 to monitor the dynamics. In different runs the phase
                 transitions might be present or not, with the system
                 finding out good solutions, or staying in poor regions
                 of algorithm space. Whenever phase transitions occur,
                 the sequence of appearances are the same. We identify
                 combinations of variables and operators which are
                 useful in measuring experience or performance in rule
                 extraction and can thus implement useful annealing of
                 the learning schedule.",
  notes =        "arXiv.org:physics Physics, abstract physics",
  size =         "11 pages",
}

Genetic Programming entries for Juan Pablo Neirotti Nestor Caticha

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