Inproceedings,
Hybridized Crossover-Based Search Techniques for Program Discovery
Proceedings of the 1995 World Conference on Evolutionary Computation, 2, page 573--578. Perth, Australia, IEEE Press, (29 November - 1 December 1995)
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.
Tags
- algorithms,
- annealing,
- candidate
- climbing;
- crossover
- crossover-based
- discovery,
- genetic
- hierarchical
- hill
- hybridized
- inter-generation
- interval
- iterated
- iterative
- methods,
- operator,
- probability,
- problems,
- program
- programming
- programming,
- search
- simulated
- single-point
- solutions,
- stochastic
- success
- techniques,
- theory,
