Fitness Landscapes and Inductive Genetic Programming

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

@InProceedings{Slavov:1997:fliGP,
  author =       "Vanyo Slavov and Nikolay I. Nikolaev",
  title =        "Fitness Landscapes and Inductive Genetic Programming",
  booktitle =    "Artificial Neural Nets and Genetic Algorithms:
                 Proceedings of the International Conference,
                 ICANNGA97",
  year =         "1997",
  editor =       "George D. Smith and Nigel C. Steele and 
                 Rudolf F. Albrecht",
  pages =        "414--418",
  address =      "University of East Anglia, Norwich, UK",
  publisher =    "Springer-Verlag",
  note =         "published in 1998",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-211-83087-1",
  broken =       "http://www.aubg.bg/faculty/cs/nikolaev/papers/icannga97.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/cache/papers/cs/1016/http:zSzzSzwww.aubg.bgzSzfacultyzSznatscizSznikolaevzSzpaperszSzicannga97.pdf/fitness-landscapes-and-inductive.pdf",
  URL =          "http://citeseer.ist.psu.edu/nikolaev97inductive.html",
  DOI =          "doi:10.1007/978-3-7091-6492-1_91",
  abstract =     "This paper proposes a study of the performance of
                 inductive genetic programming with decision trees. The
                 investigation concerns the influence of the fitness
                 function, the genetic mutation operator and the
                 categorical distribution of the examples in inductive
                 tasks on the search process. The approach uses
                 statistical correlations in order to clarify two
                 aspects: the global and the local search
                 characteristics of the structure of the fitness
                 landscape. The work is motivated by the fact that the
                 structure of the fitness landscape is the only
                 information which helps to navigate in the search space
                 of the inductive task. It was found that the analysis
                 of the landscape structure allows tuning the landscape
                 and increasing the exploratory power of the operator on
                 this landscape.",
  notes =        "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html

                 GPDT investigates fitness landscape using mutation as
                 genetic operator and calculates fitness distance
                 correlation, fitness autocorrelation and fitness
                 correlation length for random walks. This analysis
                 prompts improved choices for system components such as
                 mutation rate.",
}

Genetic Programming entries for Vanio Slavov Nikolay Nikolaev

Citations