2012/03/24 by Martin Pelikán, Pelikan, Martin, Mark W. Hauschild +3
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #G.1.6 #I.2.6 #I.2.8 #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1203.5443
openalex publication_date 2012/03/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.