- author = "Una-May O'Reilly and Franz Oppacher",
- title = "Hybridized Crossover-Based Search Techniques for Program Discovery",
- institution = "Santa Fe Institute",
- year = "1995",
- number = "95-02-007",
-
address = "1399 Hyde Park Road Santa Fe, New Mexico 87501-8943
USA
",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://www.santafe.edu/research/publications/workingpapers/95-02-007.ps",
- broken = "http://www.ai.mit.edu/people/unamay/papers/xo-hybrid.ps",
-
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 standard
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 hillclimbing component has two
options for generating candidate solutions: mutation or
crossover. When it uses crossover, mates are either
randomly selected or are individually drawn from the
population at large, or are drawn from a pool of
fittest individuals. The population pool option has
proved superior thus indicating that a combination of
population-based evolution and greedy exploitation of a
single individual has merit.
",
-
notes = "If you want the paper version contact SFI
(mat@santafe.edu) or contact una-may for a postscript
uuencoded version. All comments are welcome. Contact me
with unamay@santafe.edu.
The unabridged version of this paper is Santa Fe Institute Working Paper: 95-02-007. An abridged (6 page) version is to appear in the proceedings of the 1995 World Conference on Evolutionary Computation held in Perth, Australia, December 1-3, 1995.",
- size = "11 pages",
