Assessing the Effectiveness of Incorporating Knowledge in an Evolutionary Concept Learner

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

@InProceedings{eurogp:Divina05,
  author =       "Federico Divina",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Assessing the Effectiveness of Incorporating Knowledge
                 in an Evolutionary Concept Learner",
  booktitle =    "Proceedings of the 8th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "13--24",
  URL =          "http://www.cs.vu.nl/~divina/Publications/EuroGP-divina.pdf",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  size =         "12 pages",
  abstract =     "Classical methods for Inductive Concept Learning (ICL)
                 rely mostly on using specific search strategies, as
                 hill climbing and inverse resolution. These strategies
                 have a great exploitation power, but run the risk of
                 being incapable of escaping from local optima. An
                 alternative approach to ICL is represented by
                 Evolutionary Algorithms (EAs). EAs have a great
                 exploration power, thus they have the capability of
                 escaping from local optima, but their exploitation
                 power is rather poor. These observations suggest that
                 the two approaches are applicable to partly
                 complementary classes of learning problems. More
                 important, they indicate that a system incorporating
                 features from both approaches could benefit from the
                 complementary qualities of the approaches. In this
                 paper we experimentally validate this statement. To
                 this end, we incorporate different search strategies in
                 a framework based on EAs for ICL. Results of
                 experiments show that incorporating standard search
                 strategies helps the EAs in achieving better results.",
  notes =        "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",
}

Genetic Programming entries for Federico Divina

Citations