Genetic Programming and Deductive-Inductive Learning: A Multistrategy Approach

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

@InProceedings{icml98-ricardo,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Genetic Programming and Deductive-Inductive Learning:
                 A Multistrategy Approach",
  booktitle =    "Proceedings of the Fifteenth International Conference
                 on Machine Learning, ICML'98",
  year =         "1998",
  editor =       "Jude Shavlik",
  pages =        "10--18",
  address =      "Madison, Wisconsin, USA",
  month =        jul,
  publisher =    "Morgan Kaufmann",
  keywords =     "genetic algorithms, genetic programming, Learning in
                 Planning, Multistrategy learning",
  ISBN =         "1-55860-556-8",
  URL =          "http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz",
  size =         "9 pages",
  abstract =     "Genetic Programming (GP) is a machine learning
                 technique that was not conceived to use domain
                 knowledge for generating new candidate solutions. It
                 has been shown that GP can benefit from domain
                 knowledge obtained by other machine learning methods
                 with more powerful heuristics. However, it is not
                 obvious that a combination of GP and a knowledge
                 intensive machine learning method can work better than
                 the knowledge intensive method alone. In this paper we
                 present a multistrategy approach where an already
                 multistrategy approach ({\sc hamlet} combines
                 analytical and inductive learning) and an evolutionary
                 technique based on GP (EvoCK) are combined for the task
                 of learning control rules for problem solving in
                 planning. Results show that both methods complement
                 each other, supplying to the other method what the
                 other method lacks and obtaining better results than
                 using each method alone.",
  notes =        "ICML'98 http://www.cs.wisc.edu/icml98/ blocksworld
                 many random problems generated in order to train
                 system. No crossover, steady state population size = 2,
                 tournament size = 2",
}

Genetic Programming entries for Ricardo Aler Mur Daniel Borrajo Pedro Isasi Vinuela

Citations