R
R.H. Murga
Researcher at University of the Basque Country
Publications - 6
Citations - 1681
R.H. Murga is an academic researcher from University of the Basque Country. The author has contributed to research in topics: Bayesian network & Quality control and genetic algorithms. The author has an hindex of 6, co-authored 6 publications receiving 1564 citations.
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Journal ArticleDOI
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators
TL;DR: This paper presents crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation.
Journal ArticleDOI
Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters
TL;DR: This work tackles the problem of the search for the best Bayesian network structure, given a database of cases, using the genetic algorithm philosophy for searching among alternative structures, by assuming an ordering between the nodes of the network structures.
Journal ArticleDOI
Learning Bayesian network structures by searching for the best ordering with genetic algorithms
TL;DR: A new methodology for inducing Bayesian network structures from a database of cases based on searching for the best ordering of the system variables by means of genetic algorithms using genetic operators developed for the traveling salesman problem.
Journal ArticleDOI
Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms
TL;DR: The optimal decomposition of Bayesian networks is considered and the applicability of genetic algorithms to the problem of the triangulation of moral graphs is examined empirically.
Book ChapterDOI
Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms
TL;DR: This paper demonstrates how genetic algorithms can be used to discover the structure of a Bayesian network from a given database with cases by applying four different types of genetic algorithms to simulations of the ALARM Network.