Quantitative Analysis of Evolvability using Vertex Centralities in Phenotype Network

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Ting Hu and Wolfgang Banzhaf",
  title =        "Quantitative Analysis of Evolvability using Vertex
                 Centralities in Phenotype Network",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "733--740",
  note =         "Nominated for best paper",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908940",
  abstract =     "In an evolutionary system, robustness describes the
                 resilience to mutational and environmental changes,
                 whereas evolvability captures the capability of
                 generating novel and adaptive phenotypes. The research
                 literature has not seen an effective quantification of
                 phenotypic evolvability able to predict the
                 evolutionary potential of the search for novel
                 phenotypes. In this study, we propose to characterize
                 the mutational potential among different phenotypes
                 using the phenotype network, where vertices are
                 phenotypes and edges represent mutational connections
                 between them. In the framework of such a network, we
                 quantitatively analyse the evolvability of phenotypes
                 by exploring a number of vertex centrality measures
                 commonly used in complex networks. In our simulation
                 studies we use a Linear Genetic Programming system and
                 a population of random walkers. Our results suggest
                 that the weighted eigenvector centrality serves as the
                 best estimator of phenotypic evolvability.",
  notes =        "Memorial University

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",

Genetic Programming entries for Ting Hu Wolfgang Banzhaf