Transfer learning, soft distance-based bias, and the hierarchical BOA

Authors:
Martin Pelikan;Mark W. Hauschild;Pier Luca Lanzi
Affiliations:
Missouri Estimation of Distribution Algorithms Laboratory (MEDAL), Department of Mathematics and Computer Science, University of Missouri, St. Louis, MO;Missouri Estimation of Distribution Algorithms Laboratory (MEDAL), Department of Mathematics and Computer Science, University of Missouri, St. Louis, MO;Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Year:
2012

Citing 17
Cited 0

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Multitask Learning

Machine Learning - Special issue on inductive transfer
Learning Bayesian networks with local structure

Learning in graphical models
Morphing: combining structure and randomness

AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models

Computational Optimization and Applications
Schemata, Distributions and Graphical Models in Evolutionary Optimization

Journal of Heuristics
Finding Multimodal Solutions Using Restricted Tournament Selection

Proceedings of the 6th International Conference on Genetic Algorithms
Hybrid evolutionary algorithms on minimum vertex cover for random graphs

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Sporadic model building for efficiency enhancement of the hierarchical BOA

Genetic Programming and Evolvable Machines
Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Intelligent bias of network structures in the hierarchical BOA

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analyzing probabilistic models in hierarchical BOA

IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
Hierarchical BOA solves ising spin glasses and MAXSAT

GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Direct transfer of learned information among neural networks

AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Model accuracy in the Bayesian optimization algorithm

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Using previous models to bias structural learning in the hierarchical boa

Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.