Self-Adapting Semantic Sensitivities for Semantic Similarity Based Crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Nguyen Quang Uy and Bob McKay and Michael O'Neill and 
                 Nguyen Xuan Hoai",
  title =        "Self-Adapting Semantic Sensitivities for Semantic
                 Similarity Based Crossover",
  booktitle =    "2010 IEEE World Congress on Computational
  pages =        "4034--4040",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-6910-9",
  DOI =          "doi:10.1109/CEC.2010.5586052",
  size =         "7 pages",
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  abstract =     "This paper presents two methods for self-adapting the
                 semantic sensitivities in a recently proposed
                 semantics-based crossover: Semantic Similarity based
                 Crossover (SSC)[1]. The first self-adaptation method is
                 inspired by a self-adaptive method for controlling
                 mutation step size in Evolutionary Strategies (1/5
                 rule). The design of the second takes into account more
                 of our previous experimental observations, that SSC
                 works well only when a certain portion of events
                 successfully exchange semantically similar subtrees.
                 These two proposed methods are then tested on a number
                 of real-valued symbolic regression problems, their
                 performance being compared with SSC using predetermined
                 sensitivities and with standard crossover. The results
                 confirm the benefits of the second self-adaption
  notes =        "WCCI 2010. Also known as \cite{5586052}",

Genetic Programming entries for Quang Uy Nguyen R I (Bob) McKay Michael O'Neill Nguyen Xuan Hoai