Abstract
This paper studies genetic programming (GP) and its relation to the genetic algorithm (GA). Since the programs used as chromosomes by GP are non-homologous, GP uses a different crossover operator than GA. Thus, by modifying the GA, GP loses the theoretical foundations which have been developed for GA. This paper describes an algorithm (called EPI for evolutionary program induction) that stays within the canonical GA paradigm yet breeds programs in a similar manner to GP. EPI has been tested on three problems whose behavior under GP is known; EPI performed identically to GP over this test suite. The success of the implementation shows that the special crossover used in GP is not necessary to solve program induction using a GA.
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© 1998 Springer-Verlag Wien
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Oppacher, F., Wineberg, M. (1998). A Canonical Genetic Algorithm Based Approach to Genetic Programming. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_88
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DOI: https://doi.org/10.1007/978-3-7091-6492-1_88
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
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