Genetic breeding of non-linear optimal control strategies for broom balancing   [OC]

by

Koza, J., R. and Keane, M., A.

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Info: Proceedings of the Ninth International Conference on Analysis and Optimization of Systems. 1990 (Conference proceedings), 1990, p. 47-56
Keywords:genetic algorithms, genetic programming
Abstract:
Many seemingly different problems in machine learning, artificial intelligence, [ML] [AI] 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 [GP] 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, [GP] 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 [GP] is illustrated via an example of machine learning [ML] of the Boolean 11-multiplexer function, symbolic regression [SR] 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., [IS] and the robotics problem of controlling an autonomous mobile robot to [MR] 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 [GP] 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 [ML] of the Boolean 11-parity function.
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BibTex:
@InProceedings{Koza:1990:GPbroom,
  author =       "John R. Koza and Martin A. Keane",
  title =        "Genetic breeding of non-linear optimal control
                 strategies for broom balancing",
  booktitle =    "Proceedings of the Ninth International Conference on
                 Analysis and Optimization of Systems. 1990",
  year =         "1990",
  pages =        "47--56",
  address =      "Antibes, France",
  publisher_address = "Berlin, Germany",
  month =        jun,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  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.",
}