A memetic approach to bayesian network structure learning

Authors:
Alberto Tonda;Evelyne Lutton;Giovanni Squillero;Pierre-Henri Wuillemin
Affiliations:
INRA UMR 782 GMPA, Thiverval-Grignon, France;INRIA Saclay-Ile-de-France, AVIZ team, Orsay Cedex, France;Politecnico di Torino, DAUIN, Torino, Italy;LIP6 - Département DÉSIR, Paris
Venue:
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Year:
2013

Citing 9
Cited 0

A Bayesian Method for the Induction of Probabilistic Networks from Data

Machine Learning
Adaptive Probabilistic Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence
SMILE: Structural Modeling, Inference, and Learning Engine and GeNIe: a development environment for graphical decision-theoretic models

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
Using Bayesian networks to analyze expression data

RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Bayesian network structure learning using cooperative coevolution

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A probabilistic memetic framework

IEEE Transactions on Evolutionary Computation
Evolutionary Optimization: the GP toolkit

Evolutionary Optimization: the GP toolkit
Learning Bayesian network structures by searching for the best ordering with genetic algorithms

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bayesian networks are graphical statistical models that represent inference between data. For their effectiveness and versatility, they are widely adopted to represent knowledge in different domains. Several research lines address the NP-hard problem of Bayesian network structure learning starting from data: over the years, the machine learning community delivered effective heuristics, while different Evolutionary Algorithms have been devised to tackle this complex problem. This paper presents a Memetic Algorithm for Bayesian network structure learning, that combines the exploratory power of an Evolutionary Algorithm with the speed of local search. Experimental results show that the proposed approach is able to outperform state-of-the-art heuristics on two well-studied benchmarks.