Hybridized Crossover-Based Search Techniques for Program Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Una-May O'Reilly and Franz Oppacher",
  title =        "Hybridized Crossover-Based Search Techniques for
                 Program Discovery",
  booktitle =    "Proceedings of the 1995 World Conference on
                 Evolutionary Computation",
  year =         "1995",
  volume =       "2",
  pages =        "573--578",
  address =      "Perth, Australia",
  publisher_address = "Piscataway, NJ, USA",
  month =        "29 " # nov # " - 1 " # dec,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, simulated
                 annealing, iterative methods, search problems,
                 programming theory, hybridized crossover-based search
                 techniques, program discovery, hierarchical crossover
                 operator, single-point search algorithms, simulated
                 annealing, stochastic iterated hill climbing; candidate
                 solutions, success probability, inter-generation
  ISBN =         "0-7803-2759-4",
  broken =       "http://www.ai.mit.edu/people/unamay/papers/ec95.ps",
  URL =          "http://ieeexplore.ieee.org/iel2/3507/10438/00487447.pdf",
  size =         "6 pages",
  abstract =     "In this paper we address the problem of program
                 discovery as defined by Genetic Programming. We have
                 two major results: First, by combining a hierarchical
                 crossover operator with two traditional single point
                 search algorithms: Simulated Annealing and Stochastic
                 Iterated Hill Climbing, we have solved some problems
                 with fewer fitness evaluations and a greater
                 probability of a success than Genetic Programming.
                 Second, we have managed to enhance Genetic Programming
                 by hybridizing it with the simple scheme of hill
                 climbing from a few individuals, at a fixed interval of
                 generations. The new hill climbing component has two
                 options for generating candidate solutions: mutation or
                 crossover. When it uses crossover, mates are either
                 randomly created, randomly drawn from the population at
                 large, or drawn from a pool of fittest individuals.",
  notes =        "ICEC-95

                 This paper is an abridged version of SFI Tech Report:


Genetic Programming entries for Una-May O'Reilly Franz Oppacher