In a parallel EA one can strictly adhere to the generational clock, and wait
for all evaluations in a generation to be done. However, this idle time limits
the throughput of the algorithm and wastes computational resources.
Alternatively, an EA can be made asynchronous parallel. However, EAs using
classic recombination and selection operators (GAs) are known to suffer from an
evaluation time bias, which also influences the performance of the approach.
Model-Based Evolutionary Algorithms (MBEAs) are more scalable than classic GAs
by virtue of capturing the structure of a problem in a model. If this model is
learned through linkage learning based on the population, the learned model may
also capture biases. Thus, if an asynchronous parallel MBEA is also affected by
an evaluation time bias, this could result in learned models to be less suited
to solving the problem, reducing performance. Therefore, in this work, we study
the impact and presence of evaluation time biases on MBEAs in an asynchronous
parallelization setting, and compare this to the biases in GAs. We find that a
modern MBEA, GOMEA, is unaffected by evaluation time biases, while the more
classical MBEA, ECGA, is affected, much like GAs are.Comment: 9 pages, 3 figures, 3 tables, submitted to GECCO 202
