Evolution Strategies for Constants Optimization in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Cesar L. Alonso and Jose Luis Montana and 
                 Cruz Enrique Borges",
  title =        "Evolution Strategies for Constants Optimization in
                 Genetic Programming",
  booktitle =    "21st International Conference on Tools with Artificial
                 Intelligence, ICTAI '09",
  year =         "2009",
  month =        nov,
  pages =        "703--707",
  keywords =     "genetic algorithms, genetic programming, computer
                 program, constants optimization, evolutionary
                 computation methods, learning problems, linear genetic
                 programming approach, symbolic regression problem,
                 regression analysis",
  DOI =          "doi:10.1109/ICTAI.2009.35",
  ISSN =         "1082-3409",
  abstract =     "Evolutionary computation methods have been used to
                 solve several optimization and learning problems. This
                 paper describes an application of evolutionary
                 computation methods to constants optimization in
                 genetic programming. A general evolution strategy
                 technique is proposed for approximating the optimal
                 constants in a computer program representing the
                 solution of a symbolic regression problem. The new
                 algorithm has been compared with a recent linear
                 genetic programming approach based on straight-line
                 programs. The experimental results show that the
                 proposed algorithm improves such technique.",
  notes =        "Also known as \cite{5366517}",

Genetic Programming entries for Cesar Luis Alonso Jose Luis Montana Arnaiz Cruz Enrique Borges