# An Updated Taxonomy of Evolutionary Computation Problems using
Graph-based Evolutionary Algorithms

Submitted to CEC 2006

### Kenneth M. Bryden, Daniel A Ashlock, Steven Corns, and Justin Schonfeld

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.