On the Use of a Non-redundant Encoding for Learning Bayesian Networks from Data with a GA

We study the impact of the choice of search space for a GA that learns Bayesian networks from data. The most convenient search space is redundant and therefore allows for multiple representations of the same solution and possibly disruption during crossover. An alternative search space eliminates this redundancy, and potentially allows a more efficient search to be conducted. On the other hand, a non-redundant encoding requires a more complicated implementation. We experimentally compare several plausible approaches (GAs) to study the impact of this and other design decisions.

[1]  Bart Naudts,et al.  A comparison of predictive measures of problem difficulty in evolutionary algorithms , 2000, IEEE Trans. Evol. Comput..

[2]  David Maxwell Chickering,et al.  Large-Sample Learning of Bayesian Networks is NP-Hard , 2002, J. Mach. Learn. Res..

[3]  Silja Renooij,et al.  Probabilities for a probabilistic network: a case study in oesophageal cancer , 2002, Artif. Intell. Medicine.

[4]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[5]  Bernard Manderick,et al.  The Genetic Algorithm and the Structure of the Fitness Landscape , 1991, ICGA.

[6]  Christopher Meek,et al.  Learning Bayesian Networks with Discrete Variables from Data , 1995, KDD.

[7]  Dirk Thierens,et al.  A Skeleton-Based Approach to Learning Bayesian Networks from Data , 2003, PKDD.

[8]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  José M. Peña,et al.  On Local Optima in Learning Bayesian Networks , 2003, UAI.

[10]  Dirk Thierens,et al.  Building a GA from Design Principles for Learning Bayesian Networks , 2003, GECCO.

[11]  David Maxwell Chickering,et al.  Optimal Structure Identification With Greedy Search , 2003, J. Mach. Learn. Res..

[12]  Franz Rothlauf,et al.  Redundant Representations in Evolutionary Computation , 2003, Evolutionary Computation.

[13]  Wai Lam,et al.  LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..

[14]  Luis M. de Campos,et al.  Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs , 2011, J. Artif. Intell. Res..

[15]  Robert Castelo,et al.  On Inclusion-Driven Learning of Bayesian Networks , 2003, J. Mach. Learn. Res..

[16]  David Maxwell Chickering,et al.  Learning Bayesian Networks: The Combination of Knowledge and Statistical Data , 1994, Machine Learning.

[17]  David Maxwell Chickering,et al.  Learning Equivalence Classes of Bayesian Network Structures , 1996, UAI.

[18]  Niels Peek,et al.  Developing a Decision-Theoretic Network for a Congenital Heart Disease , 1997, AIME.

[19]  Lee Altenberg,et al.  Fitness Distance Correlation Analysis: An Instructive Counterexample , 1997, ICGA.

[20]  Dirk Thierens,et al.  Non-redundant genetic coding of neural networks , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[21]  Phil Husbands,et al.  Fitness Landscapes and Evolvability , 2002, Evolutionary Computation.

[22]  Dirk Thierens,et al.  On the Design and Analysis of Competent Selecto-recombinative GAs , 2004, Evolutionary Computation.

[23]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.