- 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 interval",
- 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: 95-02-007
",
