Guided genetic algorithm for the influence maximization problem

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

  author =       "Pavel Kromer and Jana Nowakova",
  title =        "Guided genetic algorithm for the influence
                 maximization problem",
  booktitle =    "23rd International Conference on Computing and
                 Combinatorics, COCOON 2017",
  year =         "2017",
  volume =       "10392",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "630--641",
  address =      "Hong Kong",
  month =        "3-5 " # aug,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Influence
                 maximization, Information diffusion, Social networks",
  DOI =          "doi:10.1007/978-3-319-62389-4_52",
  URL =          "",
  affiliation =  "Department of Computer Science, VSB Technical
                 University of Ostrava, Ostrava, Czech Republic",
  abstract =     "Influence maximization is a hard combinatorial
                 optimization problem. It requires the identification of
                 an optimum set of k network vertices that triggers the
                 activation of a maximum total number of remaining
                 network nodes with respect to a chosen propagation
                 model. The problem is appealing because it is provably
                 hard and has a number of practical applications in
                 domains such as data mining and social network
                 analysis. Although there are many exact and heuristic
                 algorithms for influence maximization, it has been
                 tackled by metaheuristic and evolutionary methods as
                 well. This paper presents and evaluates a new
                 evolutionary method for influence maximization that
                 employs a recent genetic algorithm for fixed-length
                 subset selection. The algorithm is extended by the
                 concept of guiding that prevents selection of
                 infeasible vertices, reduces the search space, and
                 effectively improves the evolutionary procedure.",
  source =       "Scopus",
  notes =        "Code 195859",

Genetic Programming entries for Pavel Kromer Jana Nowakova