Visual Analytics of EA Data

Evelyne Lutton 1 Jean-Daniel Fekete 1
1 AVIZ - Analysis and Visualization
Inria Saclay - Ile de France
Abstract : An experimental analysis of evolutionary algorithms usually generates a huge amount of multidimensional data, including numeric and symbolic data. It is difficult to efficiently navigate in such a set of data, for instance to be able to tune the parameters or evaluate the efficiency of some operators. Usual features of existing EA visualisation systems consist in visualising time- or generation-dependent curves (fitness, diversity, or other statistics). When dealing with genomic information, the task becomes even more difficult, as a convenient visualisation strongly depends on the considered fitness landscape. In this latter case the raw data are usually sets of suc- cessive populations of points of a complex multidimensional space. The purpose of this paper is to evaluate the potential interest of a recent visual analytics tool for navigating in complex sets of EA data, and to sketch future developements of this tool, in order to better adapt it to the needs of EA experimental analysis.
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Communication dans un congrès
Genetic and Evolutionary Computation Conference, GECCO 2011, Jul 2011, Dublin, Ireland. 2011
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Contributeur : Evelyne Lutton <>
Soumis le : jeudi 17 novembre 2011 - 17:43:11
Dernière modification le : jeudi 9 février 2017 - 15:48:05
Document(s) archivé(s) le : vendredi 16 novembre 2012 - 11:21:50

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Evelyne Lutton, Jean-Daniel Fekete. Visual Analytics of EA Data. Genetic and Evolutionary Computation Conference, GECCO 2011, Jul 2011, Dublin, Ireland. 2011. <hal-00642300>

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