Investigating Problem Hardness of Real Life Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Vanneschi:2007:GPTP,
  author =       "Leonardo Vanneschi",
  title =        "Investigating Problem Hardness of Real Life
                 Applications",
  booktitle =    "Genetic Programming Theory and Practice {V}",
  year =         "2007",
  editor =       "Rick L. Riolo and Terence Soule and Bill Worzel",
  series =       "Genetic and Evolutionary Computation",
  chapter =      "7",
  pages =        "107--125",
  address =      "Ann Arbor",
  month =        "17-19" # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-387-76308-8",
  DOI =          "doi:10.1007/978-0-387-76308-8_7",
  size =         "18 pages",
  abstract =     "This chapter represents a first attempt to
                 characterise the fitness landscapes of real-life
                 Genetic Programming applications by means of a
                 predictive algebraic difficulty indicator. The
                 indicator used is the Negative Slope Coefficient, whose
                 efficacy has been recently empirically demonstrated on
                 a large set of hand-tailored theoretical test functions
                 and well known GP benchmarks. The real-life problems
                 studied belong to the field of Biomedical applications
                 and consist of automatically assessing a mathematical
                 relationship between a set of molecular descriptors
                 from a given dataset of drugs and some important
                 pharmacokinetic parameters. The parameters considered
                 here are Human Oral Bioavailability, Median Oral Lethal
                 Dose, and Plasma Protein Binding levels. The
                 availability of good prediction tools for
                 pharmacokinetics parameters like these is critical for
                 optimising the efficiency of therapies, maximising
                 medical success rate and minimizing toxic effects. The
                 experimental results presented in this chapter show
                 that the Negative Slope Coefficient seems to be a
                 reasonable tool to characterise the difficulty of these
                 problems, and can be used to choose the most effective
                 Genetic Programming configuration (fitness function,
                 representation, parameters' values) from a set of given
                 ones.",
  notes =        "part of \cite{Riolo:2007:GPTP} published 2008",
  affiliation =  "Dipartimento di Informatica, Sistemistica e
                 Comunicazione (D.I.S.Co.), University of Milano,
                 Bicocca Milan, Italy",
}

Genetic Programming entries for Leonardo Vanneschi

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