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.

翻译：在并行进化算法中，可以严格遵循世代时钟，等待一个世代中的所有评估完成。然而，这种空闲时间限制了算法的吞吐量并浪费了计算资源。另一种选择是采用异步并行的进化算法。然而，使用经典重组和选择算子（遗传算法）的进化算法已知会受到评估时间偏差的影响，这也会影响该方法的性能。基于模型的进化算法（MBEAs）通过捕获问题结构于模型中，比经典遗传算法更具可扩展性。如果该模型通过基于种群的连接学习进行学习，则学习到的模型也可能捕获偏差。因此，如果异步并行MBEA也受到评估时间偏差的影响，这可能导致学习到的模型不太适合解决问题，从而降低性能。因此，在本工作中，我们研究了评估时间偏差在异步并行化设置中对MBEAs的影响和存在性，并将其与遗传算法中的偏差进行比较。我们发现，现代MBEA——GOMEA不受评估时间偏差的影响，而更经典的MBEA——ECGA则受到影响，这与遗传算法类似。