An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction

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@Misc{Castelli:2012:arXiv,
  title =        "An Efficient Genetic Programming System with Geometric
                 Semantic Operators and its Application to Human Oral
                 Bioavailability Prediction",
  author =       "Mauro Castelli and Luca Manzoni and 
                 Leonardo Vanneschi",
  howpublished = "arXiv",
  year =         "2012",
  month =        "12 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  bibdate =      "2012-10-10",
  URL =          "http://arxiv.org/abs/1208.2437",
  size =         "10 pages",
  abstract =     "Very recently new genetic operators, called geometric
                 semantic operators, have been defined for genetic
                 programming. Contrarily to standard genetic operators,
                 which are uniquely based on the syntax of the
                 individuals, these new operators are based on their
                 semantics, meaning with it the set of input-output
                 pairs on training data. Furthermore, these operators
                 present the interesting property of inducing a unimodal
                 fitness landscape for every problem that consists in
                 finding a match between given input and output data
                 (for instance regression and classification).
                 Nevertheless, the current definition of these operators
                 has a serious limitation: they impose an exponential
                 growth in the size of the individuals in the
                 population, so their use is impossible in practice.
                 This paper is intended to overcome this limitation,
                 presenting a new genetic programming system that
                 implements geometric semantic operators in an extremely
                 efficient way. To demonstrate the power of the proposed
                 system, we use it to solve a complex real-life
                 application in the field of pharmacokinetic: the
                 prediction of the human oral bioavailability of
                 potential new drugs. Besides the excellent performances
                 on training data, which were expected because the
                 fitness landscape is unimodal, we also report an
                 excellent generalisation ability of the proposed
                 system, at least for the studied application. In fact,
                 it outperforms standard genetic programming and a wide
                 set of other well-known machine learning methods.",
}

Genetic Programming entries for Mauro Castelli Luca Manzoni Leonardo Vanneschi

Citations