Hybridized crossover-based search techniques for program discovery

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 sol...

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Bibliographic Details
Main Authors: O'Reilly, U.-M., Oppacher, F.
Format: Conference Proceeding
Language:English
Subjects:
Computational modeling
Computer science
Drives
Genetic programming
Hybrid power systems
Random number generation
Search methods
Simulated annealing
Stochastic processes
Temperature
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Summary: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.
DOI:10.1109/ICEC.1995.487447