Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Koza:1995:2ss,

• author = "John R. Koza",
• title = "Two Ways of Discovering the Size and Shape of a Computer Program to Solve a Problem",
• booktitle = "Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95)",
• year = "1995",
• editor = "Larry J. Eshelman",
• pages = "287--294",
• address = "Pittsburgh, PA, USA",
• publisher_address = "San Francisco, CA, USA",
• month = "15-19 " # jul,
• publisher = "Morgan Kaufmann",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-55860-370-0",
• URL = "http://www.genetic-programming.com/jkpdf/icga1995.pdf",
• abstract = "The requirement that the user predetermine the size and shape of the ultimate solution to a problem has been a bane of automated machine learning from the earliest times. This paper compares two techniques for automatically discovering, during a run of genetic programming, the architecture of a multi-part computer program while concurrently solving the problem. In the first technique, called evolutionary selection, the initial random population is architecturally diverse and there is a competition during the run among the various architectures while they are trying to solve the problem. The second technique, called evolution of architecture, employs six new architecture-altering operations that provide a way to evolve the architecture of a multi-part program in the sense of actually changing the architecture of the program dynamically during the run. The new architecture-altering operations are motivated by the naturally occurring operation of gene duplication, as described in Susumu Ohno's provocative book Evolution by Means of Gene Duplication.",

}

Genetic Programming entries for John Koza

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