Unveiling evolutionary algorithm representation with DU maps

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@Article{Medvet:2018:GPEM,
  author =       "Eric Medvet and Marco Virgolin and Mauro Castelli and 
                 Peter A. N. Bosman and Ivo Goncalves and Tea Tusar",
  title =        "Unveiling evolutionary algorithm representation with
                 {DU} maps",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2018",
  volume =       "19",
  number =       "3",
  pages =        "351--389",
  month =        sep,
  note =         "Special issue on genetic programming, evolutionary
                 computation and visualization",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution, GE, WHGE, SGE, Geometric Semantic Genetic
                 Programming, GSGP, Gene-pool Optimal Mixing
                 Evolutionary Algorithm, GOMEA, Neuro-Evolution of
                 Augmenting Topologies, NEAT, Representation, Diversity,
                 Usage, Visualization, Heat maps",
  ISSN =         "1389-2576",
  URL =          "https://doi.org/10.1007/s10710-018-9332-5",
  DOI =          "doi:10.1007/s10710-018-9332-5",
  size =         "39 pages",
  abstract =     "Evolutionary algorithms (EAs) have proven to be
                 effective in tackling problems in many different
                 domains. However, users are often required to spend a
                 significant amount of effort in fine-tuning the EA
                 parameters in order to make the algorithm work. In
                 principle, visualization tools may be of great help in
                 this laborious task, but current visualization tools
                 are either EA-specific, and hence hardly available to
                 all users, or too general to convey detailed
                 information. In this work, we study the Diversity and
                 Usage map (DU map), a compact visualization for
                 analysing a key component of every EA, the
                 representation of solutions. In a single heat map, the
                 DU map visualizes for entire runs how diverse the
                 genotype is across the population and to which degree
                 each gene in the genotype contributes to the solution.
                 We demonstrate the generality of the DU map concept by
                 applying it to six EAs that use different
                 representations (bit and integer strings, trees,
                 ensembles of trees, and neural networks). We present
                 the results of an online user study about the usability
                 of the DU map which confirm the suitability of the
                 proposed tool and provide important insights on our
                 design choices. By providing a visualization tool that
                 can be easily tailored by specifying the diversity (D)
                 and usage (U) functions, the DU map aims at being a
                 powerful analysis tool for EAs practitioners, making
                 EAs more transparent and hence lowering the barrier for
                 their use.",
}

Genetic Programming entries for Eric Medvet Marco Virgolin Mauro Castelli Peter A N Bosman Ivo Goncalves Tea Tusar

Citations