  Chimera graphs define the topology of one of the first commercially available
quantum computers. A variety of optimization problems have been mapped to this
topology to evaluate the behavior of quantum enhanced optimization heuristics
in relation to other optimizers, being able to efficiently solve problems
classically to use them as benchmarks for quantum machines. In this paper we
investigate for the first time the use of Evolutionary Algorithms (EAs) on
Ising spin glass instances defined on the Chimera topology. Three genetic
algorithms (GAs) and three estimation of distribution algorithms (EDAs) are
evaluated over $1000$ hard instances of the Ising spin glass constructed from
Sidon sets. We focus on determining whether the information about the topology
of the graph can be used to improve the results of EAs and on identifying the
characteristics of the Ising instances that influence the success rate of GAs
and EDAs.
