Pairwise Comparison of Hypotheses Coverings as a Natural Mean Against Undesirable Niching in Evolutionary Inductive Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  title =        "Pairwise Comparison of Hypotheses Coverings as a
                 Natural Mean Against Undesirable Niching in
                 Evolutionary Inductive Learning",
  author =       "Krzysztof Krawiec",
  institution =  "Institute of Computing Science, Poznan University of
  type =         "Research Report",
  year =         "2001",
  number =       "RA-005/2001",
  address =      "Poland",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  citeseer-isreferencedby = "oai:CiteSeerPSU:421784;
                 oai:CiteSeerPSU:67952; oai:CiteSeerPSU:477598",
  citeseer-references = "oai:CiteSeerPSU:18963; oai:CiteSeerPSU:377509;
                 oai:CiteSeerPSU:356583; oai:CiteSeerPSU:212034;
                 \cite{oai:CiteSeerPSU:451901}; oai:CiteSeerPSU:446777;
                 \cite{oai:CiteSeerPSU:451286}; oai:CiteSeerPSU:457380",
  annote =       "The Pennsylvania State University CiteSeer Archives",
  language =     "en",
  oai =          "oai:CiteSeerPSU:458532",
  rights =       "unrestricted",
  abstract =     "This report summarizes the results of research on the
                 use of evolutionary learning for solving pattern
                 recognition problems. The general idea consists in
                 evolutionary search in the space of pattern recognition
                 programs. The whole body of results described here was
                 obtained in the improved version of GPVIS environment
                 [15]. In particular, this report describes the 2.0
                 version of the environment and is devoted in a great
                 part to the extensions beyond the standard genetic
                 programming introduced into GPVIS, including the novel
                 method of hypothesis evaluation proposed for
                 evolutionary learning. This work focuses on reasoning
                 from pictorial information based on evolutionary
                 computation, or, to be more precise, on the paradigm of
                 genetic programming [12]. The outline of the method is
                 as follows. The genetic search engine performs the
                 search through the space of image processing and
                 analysis programs. The programs have the form of
                 expressions formulated in a specialized language called
                 GPVISL (Genetic Programming for Visual Learning
                 language). The genetic search engine realizes the
                 selection of parent solutions (individuals), which are
                 then crossed over and mutated to obtain the next
                 generation of solutions. The selection is done w.r.t.
                 the value of evaluation (fitness) function. A solution
                 is evaluated by testing its behavior on a set of
                 fitness cases, which are equivalent to images in this
                 context. The fitness function is the percentage of
                 hits[14], i.e. of the correct decisions (recognitions)
                 made by the system.",
  size =         "28 pages",

Genetic Programming entries for Krzysztof Krawiec