Using genetic programming to learn and improve control knowledge

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{aler:2002:AI,
  author =       "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
  title =        "Using genetic programming to learn and improve control
                 knowledge",
  journal =      "Artificial Intelligence",
  year =         "2002",
  volume =       "141",
  number =       "1-2",
  pages =        "29--56",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Speedup
                 learning, Multi-strategy learning, Planning",
  URL =          "http://scalab.uc3m.es/~dborrajo/papers/aij-evock.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/511810.html",
  DOI =          "doi:10.1016/S0004-3702(02)00246-1",
  abstract =     "The purpose of this article is to present a
                 multi-strategy approach to learn heuristics for
                 planning. This multi-strategy system, called
                 HAMLET-EVOCK, combines a learning algorithm specialised
                 in planning () and a genetic programming (GP) based
                 system (: Evolution of Control Knowledge). Both systems
                 are able to learn heuristics for planning on their own,
                 but both of them have weaknesses. Based on previous
                 experience and some experiments performed in this
                 article, it is hypothesised that handicaps are due to
                 its example-driven operators and not having a way to
                 evaluate the usefulness of its control knowledge. It is
                 also hypothesized that even if control knowledge is
                 sometimes incorrect, it might be easily correctable.
                 For this purpose, a GP-based stage is added, because of
                 its complementary biases: GP genetic operators are not
                 example-driven and it can use a fitness function to
                 evaluate control knowledge. and are combined by seeding
                 initial population with control knowledge. It is also
                 useful for to start from a knowledge-rich population
                 instead of a random one. By adding the GP stage to ,
                 the number of solved problems increases from 58% to 85%
                 in the blocks world and from 50% to 87% in the
                 logistics domain (0% to 38% and 0% to 42% for the
                 hardest instances of problems considered).",
  notes =        "Hamlet, EvoCK, PRODIGY 4.0",
}

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

Citations