Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming

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

@InProceedings{koza:adf,

• author = "John R. Koza",
• title = "Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming",
• editor = "Stephanie Forrest",
• publisher_address = "San Mateo, CA, USA",
• year = "1993",
• booktitle = "Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93",
• publisher = "Morgan Kaufmann",
• pages = "295--302",
• address = "University of Illinois at Urbana-Champaign",
• month = "17-21 " # jul,
• keywords = "genetic algorithms, genetic programming, ADF",
• URL = "http://www.genetic-programming.com/jkpdf/icga1993.pdf",
• size = "8 pages",
• abstract = "This paper describes an approach for automatically decomposing a problem into subproblems and then automatically discovering reusable subroutines, and a way of assembling the results produced by these subroutines in order to solve a problem. The approach uses genetic programming with automatic function definition. Genetic programming provides a way to genetically breed a computer program to solve a problem. Automatic function definition enables genetic programming to define potentially useful subroutines dynamically during a run. The approach is applied to an illustrative problem. Genetic programming with automatic function definition reduced the computational effort required to learn a solution to the problem by a factor of 2.0 as compared to genetic programming without automatic function definition. Similarly, the average structural complexity of the solution was reduced by about 21percent.",
• notes = "Comparison of GP and GP+Automatic Function definition for San Mateo trail ants, finds improvement of 1:2 in number of fitness cases required and 21% reduction is size of eventual s-expressions. NO CASE made that either cases are using optimal parameters.",

}

Genetic Programming entries for John Koza

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