During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem. To overcome this issue, we consider a new BOA-based optimisation approach (FBOA) which explores the fact that patterns of PGM adjustments can be used as a guide to reduce the frequency of PGM updates because significant changes in PGM structure might not occur so frequently, and because they can be particularly sparse at the end of evolution. In the present paper, this new approach is scrutinised in the search space of an NK-landscape optimisation problem for medium and large-size instances. Average gaps and success rates as well as the correlation between the landscape ruggedness of the problem and the expected runtime of FBOA and BOA are presented for medium-size instances. For large-size instances, optimisation results from FBOA and BOA are compared. The experiments show that, despite our FBOA being of almost three times faster than BOA, it still produces competitive optimisation results.

在进化过程中，基于概率分布的算法用于生成新个体，由于对概率分布估计值进行了密集的计算，因此特别是在使用概率图模型（PGM）时，会遭受计算量的负担。例如，在贝叶斯优化算法（BOA）中，通过给定的解决方案样本确定最佳贝叶斯网络结构是一个NP难题。为解决此问题，我们考虑了一种新的基于BOA的优化方法（FBOA），该方法探索了以下事实：PGM调整模式可以用作减少PGM更新频率的指南，因为PGM结构可能不会如此频繁地发生重大变化，因为它们在开发结束时可能会特别稀疏。在本文中，对于中型和大型实例，在NK景观优化问题的搜索空间中仔细研究了这种新方法。对于中等大小的实例，提供了平均差距和成功率，以及问题的景观坚固性与FBOA和BOA的预期运行时间之间的相关性。对于大型实例，将比较FBOA和BOA的优化结果。实验表明，尽管我们的FBOA比BOA快了将近三倍，但它仍然产生了竞争性的优化结果。比较了FBOA和BOA的优化结果。实验表明，尽管我们的FBOA比BOA快了将近三倍，但它仍然产生了竞争性的优化结果。比较了FBOA和BOA的优化结果。实验表明，尽管我们的FBOA比BOA快了将近三倍，但它仍然产生了竞争性的优化结果。