无参数分层BOA
Parameter-less hierarchical BOA
摘要：
无参数分层贝叶斯优化算法（hBOA）使hBOA的使用，而不需要调整参数来解决每个问题的实例。hBOA中有三个关键参数：（1）选择压力，（2）限制性比赛的窗口大小，以及（3）人口规模。虽然选择压力和窗口大小都影响hBOA性能，但性能应保持低阶多项式，并对这两个参数进行标准选择。然而，没有一个标准的人口规模可以解决所有感兴趣的问题，因此必须以不同的方式消除人口规模。为了消除种群规模，无参数hBOA采用无参数遗传算法的种群规模技术。基于现有的理论，无参数的hBOA应该能够解决几乎可分解和分层的问题，在二次或次二次函数的数量评估，而不需要设置任何参数。提出了一些实验来验证无参数hBOA的可扩展性。
英文摘要：
The parameter-less hierarchical Bayesian optimization algorithm (hBOA) enables the use of hBOA without the need for tuning parameters for solving each problem instance. There are three crucial parameters in hBOA: (1) the selection pressure, (2) the window size for restricted tournaments, and (3) the population size. Although both the selection pressure and the window size influence hBOA performance, performance should remain low-order polynomial with standard choices of these two parameters. However, there is no standard population size that would work for all problems of interest and the population size must thus be eliminated in a different way. To eliminate the population size, the parameter-less hBOA adopts the population-sizing technique of the parameter-less genetic algorithm. Based on the existing theory, the parameter-less hBOA should be able to solve nearly decomposable and hierarchical problems in quadratic or subquadratic number of function evaluations without the need for setting any parameters whatsoever. A number of experiments are presented to verify scalability of the parameter-less hBOA.
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