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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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",
  editor =       "Alain Bensoussan and Jacques Louis Lions",
  series =       "Lecture Notes in Control and Information Sciences",
  pages =        "47--56",
  address =      "Antibes, France",
  publisher_address = "Berlin, Germany",
  month =        jun,
  publisher =    "Springer-Verlag",
  ISBN =         "0-387-52630-7",
  isbn13 =       "978-3-540-52630-8",
  keywords =     "genetic algorithms, genetic programming",
  url_abstract = "http://www.genetic-programming.com/jkpdf/caos1990abstract.pdf",
  DOI =          "doi:10.1007/BFb0120027",
  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.",
  abstract =     "This paper describes a search for the time-optimal
                 bang bang control strategy for the three dimensional
                 broom balancing (inverted pendulum) problem by
                 genetically breeding populations of control strategies
                 using a recently developed new 'genetic computing'
                 paradigm. The new paradigm produces results in the form
                 of a control strategy consisting of a composition of
                 functions, including arithmetic operations, conditional
                 logical operations, and mathematical functions. This
                 control strategy takes the problem's state variables as
                 its input and generates the direction from which to
                 apply the bang bang force as its output.",
  affiliation =  "Stanford University Computer Science Department 94305
                 Stanford California USA",
}

Genetic Programming entries for John Koza Martin A Keane

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