Skip to main content
Article thumbnail
Location of Repository

Meta-Genetic Programming: Co-evolving the Operators of Variation

By Bruce Edmonds

Abstract

The standard Genetic Programming approach is augmented by co-evolving the genetic operators. To do this the operators are coded as trees of indefinite length. In order for this technique to work, the language that the operators are defined in must be such that it preserves the variation in the base population. This technique can varied by adding further populations of operators and changing which populations act as operators for others, including itself, thus to provide a framework for a whole set of augmented GP techniques. The technique is tested on the parity problem. The pros and cons of the technique are discussed

Topics: Artificial Intelligence
Year: 1998
OAI identifier: oai:cogprints.org:513
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://cogprints.org/513/5/mgp... (external link)
  • http://cogprints.org/513/1/mgp... (external link)
  • http://cogprints.org/513/ (external link)
  • Suggested articles

    Citations

    1. (1995). Adaptive and Self-Adaptive Evolutionary Computations,
    2. (1996). At Home in the Universe: the search for laws of complexity.
    3. (1997). Comparing Subtree Crossover with Macromutation.
    4. (1994). Controlling crossover through inductive learning.
    5. (1997). Fitness Causes Bloat.
    6. (1995). Fundamental Limitations on Search Algorithms -Evolutionary Computing in Perspective.
    7. (1992). Genetic Programming: On the Programming of Computers by Natural Selection.
    8. (1991). Meta-Evolutionary Programming.
    9. (1985). Properties of the bucket brigade.
    10. (1996). Two Self-adaptive Crossover Operators for Genetic Programming.
    11. (1989). Varying the probability of mutation in the genetic algorithm.

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.