  An automated technique has recently been proposed to transfer learning in the
hierarchical Bayesian optimization algorithm (hBOA) based on distance-based
statistics. The technique enables practitioners to improve hBOA efficiency by
collecting statistics from probabilistic models obtained in previous hBOA runs
and using the obtained statistics to bias future hBOA runs on similar problems.
The purpose of this paper is threefold: (1) test the technique on several
classes of NP-complete problems, including MAXSAT, spin glasses and minimum
vertex cover; (2) demonstrate that the technique is effective even when
previous runs were done on problems of different size; (3) provide empirical
evidence that combining transfer learning with other efficiency enhancement
techniques can often yield nearly multiplicative speedups.
