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Graph based evolutionary algorithms use combinatorial graphs to impose a topology or "geographic structure" on an evolving population. It has been demonstrated that, for a fixed problem, time to solution varies substantially with the choice of graph. This variation is not simple with very different graphs yielding faster solution times for different problems. Normalized time to solution for many graphs thus forms an objective character that can be used for classifying the type of a problem, separate from its hardness measured with average time to solution. This study uses fifteen combinatorial graphs to classify 40 evolutionary computation problems. The resulting classification is done using neighbor joining, and the results are also displayed using non-linear projection. The different methods of grouping evolutionary computation problems into similar types exhibit substantial agreement. Numerical optimization problems form a close grouping while some other groups of problems scatter across the taxonomy. This paper updates an earlier taxonomy of 23 problems and introduces new classification techniques.