Hybridized crossover-based search techniques for program discovery
O'Reilly, U.-M.
Oppacher, F.
Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont. ;
This paper appears in: Evolutionary Computation, 1995., IEEE International Conference on
Publication Date: 29 Nov-1 Dec 1995
Volume: 2,
On page(s): 573-578 vol.2
Meeting Date: 11/29/1995 - 12/01/1995
Location: Perth, WA, Australia
ISBN: 0-7803-2759-4
References Cited: 5
INSPEC Accession Number: 5250185
Digital Object Identifier: 10.1109/ICEC.1995.487447
Current Version Published: 2002-08-06
Abstract
Addresses the problem of program discovery as defined by genetic
programming. 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 by
processing fewer candidate solutions and with a greater probability of
success than genetic programming. We have also enhanced genetic
programming by hybridizing it with the simple idea of hill climbing from
a few individuals, at a fixed interval of generations
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