Genetic programming as a means for programming computers by natural selection

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{koza:1994:SandC,

• author = "John R. Koza",
• title = "Genetic programming as a means for programming computers by natural selection",
• journal = "Statistics and Computing",
• year = "1994",
• volume = "4",
• number = "2",
• month = jun,
• pages = "87--112",
• keywords = "genetic algorithms, genetic programming, crossover, hierarchical automatic function definition, symbolic regression, Boolean 11-multiplexer, econometric exchange equation, Boolean 11-parity",
• ISSN = "0960-3174",
• url_short = "http://www.genetic-programming.com/jkpdf/scjournallong.pdf",
• url_long = "http://www.genetic-programming.com/jkpdf/scjournalshort.pdf",
• URL = "https://rdcu.be/dmw25",
• DOI = "doi:10.1007/BF00175355",
• size = "26 pages",
• abstract = "Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function, symbolic regression of the econometric exchange equation from noisy empirical data, the control problem of backing up a tractor-trailer truck, the classification problem of distinguishing between two intertwined spirals., and the robotics problem of controlling an autonomous mobile robot to find a box in the middle of an irregular room and move the box to the wall. Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run - much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.",
• publisher = "Chapman \& Hall, London",
• notes = "Special issue on Evolutionary Programming, Guest editor Zbigniew Michalewicz",

}

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