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