PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Visualizing evolutionary computation
Advances in evolutionary computing
GAVEL - a new tool for genetic algorithm visualization
IEEE Transactions on Evolutionary Computation
Graphdice: a system for exploring multivariate social networks
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Visual analysis of population scatterplots
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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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 successive 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.