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.
@article{arxiv.1203.5443,
title = {Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA},
author = {Martin Pelikan and Mark W. Hauschild and Pier Luca Lanzi},
journal= {arXiv preprint arXiv:1203.5443},
year = {2012}
}
Comments
Accepted at Parallel Problem Solving from Nature (PPSN XII), 10 pages. arXiv admin note: substantial text overlap with arXiv:1201.2241