Diversity in Multipopulation Genetic Programming

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@InProceedings{tomassini:2003:gecco,
  author =       "Marco Tomassini and Leonardo Vanneschi and 
                 Francisco Fern{\'a}ndez and Germ{\'a}n Galeano",
  title =        "Diversity in Multipopulation Genetic Programming",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1812--1813",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, poster",
  URL =          "http://personal.disco.unimib.it/Vanneschi/GECCO_2003_Diversity.pdf",
  DOI =          "doi:10.1007/3-540-45110-2_77",
  abstract =     "In the past few years, we have done a systematic
                 experimental investigation of the behavior of
                 multipopulation GP [2] and we have empirically observed
                 that distributing the individuals among several loosely
                 connected islands allows not only to save computation
                 time, due to the fact that the system runs on multiple
                 machines, but also to find better solution quality.
                 These results have often been attributed to better
                 diversity maintenance due to the periodic migration of
                 groups of {"}good{"} individuals among the
                 subpopulations. We also believe that this might be the
                 case and we study the evolution of diversity in
                 multi-island GP. All the diversity measures that we use
                 in this paper are based on the concept of entropy of a
                 population , defined as . If we are considering
                 phenotypic diversity, we define Fj as the fraction of
                 individuals in having a certain fitness , where is the
                 total number of fitness values in . In this case, the
                 entropy measure will be indicated as or simply Hp. To
                 define genotypic diversity, we use two different
                 techniques. The first one consists in partitioning
                 individuals in such a way that only identical
                 individuals belong to the same group. In this case, we
                 have considered Fj as the fraction of trees in the
                 population having a certain genotype , where is the
                 total number of genotypes in and the entropy measure
                 will be indicated as or simply HG. The second technique
                 consists in defining a distance measure, able to
                 quantify the genotypic diversity between two trees. In
                 this case, Fj is the fraction of individuals having a
                 given distance from a fixed tree (called origin), where
                 is the total number of distance values from the origin
                 appearing in and the entropy measure will be indicated
                 as or simply Hg. The tree distance used is Ekart's and
                 Nemeth's definition [1].",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",
}

Genetic Programming entries for Marco Tomassini Leonardo Vanneschi Francisco Fernandez de Vega German Galeano

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