Discovery of a main program and reusable subroutines using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Koza:1993:mprsGP,

• author = "John R. Koza",
• title = "Discovery of a main program and reusable subroutines using genetic programming",
• booktitle = "Proceedings of the Fifth Workshop on Neural Networks: An International Conference on Computational Intelligence: Neural Networks, Fuzzy Systems, Evolutionary Programming, and Virtual Reality",
• year = "1993",
• pages = "109--118",
• publisher_address = "San Diego, CA, USA",
• organisation = "The Society for Computer Simulation",
• keywords = "genetic algorithms, genetic programming, ADF",
• URL = "http://www.genetic-programming.com/jkpdf/simtec1993.pdf",
• oai = "oai:CiteSeerXPSU:10.1.1.140.5520",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.5520",
• size = "11 pages",
• abstract = "This paper describes an approach for automatically decomposing a problem into subproblems, automatically creating reusable subroutines to solve the subproblems, and automatically assembling the results produced by the subroutines in order to solve the problem. The approach uses genetic programming with the recently developed additional facility of automatic function definition. Genetic programming provides a way to genetically breed a computer program to solve a problem and automatic function definition enables genetic programming to create reusable subroutines dynamically during a run. The approach is applied to an illustrative problem containing a considerable amount of regularity. Solutions to the problem produced using automatic function definition are considerably smaller in size and require processing of considerably fewer individuals than is the case without automatic function definition. Specifically, the average program size for a solution to the problem without using automatic function definition is 3.65 times larger than the size for a solution when using automatic function definition. The number of individuals required to be processed to yield a solution with 99% probability without automatic function definition is 9.09 times larger than the equivalent number required with automatic function definition.",

}

Genetic Programming entries for John Koza

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