A Genetic Approach to the Truck Backer Upper Problem and the Inter-Twined Spiral Problem

by

Koza, J., R.

Literature search on Evolutionary ComputationBBase ©1999-2013, Rasmus K. Ursem
     Home · Search · Adv. search · Authors · Login · Add entries   Webmaster
Note to authors: Please submit your bibliography and contact information - online papers are more frequently cited.

Info: Proceedings of IJCNN International Joint Conference on Neural Networks (Conference proceedings), 1992, p. 310-318
Keywords:genetic algorithms, genetic programming, connectionism
Abstract:
ABSTRACT Neural networks [NN] are a biologically motivated problem-solving paradigm that has proven successful in robustly solving a variety of problems. This paper describes another biologically motivated paradigm, namely genetic programming, [GP] which can also solve a variety of problems. This paper explains genetic programming [GP] and applies it to two well-know benchmark problems from the field of neural networks. [NN] The truck backer upper problem is a multi-dimensional control problem and the inter-twined spirals problem [IS] is a challenging classification problem.
Notes:
IJCNN-92
Author(s) DL:Online papers for Koza, J., R.
Internet search:Search Google
Search Google Scholar
Search Citeseer using Google
Search Google for PDF
Search Google Scholar for PDF
Search Citeseer for PDF using Google

Review item:

Mark as doublet (will be reviewed)

Print entry



BibTex:
@InProceedings{Koza92,
  author =       "John R. Koza",
  title =        "A Genetic Approach to the Truck Backer Upper Problem
                 and the Inter-Twined Spiral Problem",
  booktitle =    "Proceedings of IJCNN International Joint Conference on
                 Neural Networks",
  volume =       "IV",
  pages =        "310--318",
  year =         "1992",
  publisher_address = "Piscataway, NJ, USA",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming,
                 connectionism",
  abstract =     "ABSTRACT Neural networks are a biologically motivated
                 problem-solving paradigm that has proven successful in
                 robustly solving a variety of problems. This paper
                 describes another biologically motivated paradigm,
                 namely genetic programming, which can also solve a
                 variety of problems. This paper explains genetic
                 programming and applies it to two well-know benchmark
                 problems from the field of neural networks. The truck
                 backer upper problem is a multi-dimensional control
                 problem and the inter-twined spirals problem is a
                 challenging classification problem.",
  notes =        "IJCNN-92

                 ",
}