  This paper provides an in-depth empirical analysis of several evolutionary
algorithms on the one-dimensional spin glass model with power-law interactions.
The considered spin glass model provides a mechanism for tuning the effective
range of interactions, what makes the problem interesting as an algorithm
benchmark. As algorithms, the paper considers the genetic algorithm (GA) with
twopoint and uniform crossover, and the hierarchical Bayesian optimization
algorithm (hBOA). hBOA is shown to outperform both variants of GA, whereas GA
with uniform crossover is shown to perform worst. The differences between the
compared algorithms become more significant as the problem size grows and as
the range of interactions decreases. Unlike for GA with uniform crossover, for
hBOA and GA with twopoint crossover, instances with short-range interactions
are shown to be easier. The paper also points out interesting avenues for
future research.
