  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.
