Diversity in Multipopulation Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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

Citations