Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Control of selective perception using Bayes nets and decision theory
International Journal of Computer Vision - Special issue on active vision II
Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Real-world applications of Bayesian networks
Communications of the ACM
Applying Bayesian networks to information retrieval
Communications of the ACM
Abductive reasoning in Bayesian belief networks using a genetic algorithm
Pattern Recognition Letters - Special issue on genetic algorithms
Approximating MAPs for belief networks is NP-hard and other theorems
Artificial Intelligence
Directing genetic algorithms for probabilistic reasoning through reinforcement learning
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Theoretical aspects of evolutionary computing
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
An efficient algorithm for finding the M most probable configurationsin probabilistic expert systems
Statistics and Computing
Convergence Models of Genetic Algorithm Selection Schemes
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Genetic Algorithms for Belief Network Inference: The Role of Scaling and Niching
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
An evolutionary computing approach to probabilistic reasoning on bayesian networks
Evolutionary Computation
COMPARISON OF TWO TYPES OF EVENT BAYESIAN NETWORKS: A CASE STUDY
Applied Artificial Intelligence
Journal of Artificial Intelligence Research
IEEE Transactions on Knowledge and Data Engineering
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Real time estimation of Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
An ACO algorithm for the most probable explanation problem
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
IEEE Transactions on Evolutionary Computation
Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
Generalized crowding for genetic algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Journal of Automated Reasoning
Adaptive generalized crowding for genetic algorithms
Information Sciences: an International Journal
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Constraints occur in many application areas of interest to evolutionary computation. The area considered here is Bayesian networks (BNs), which is a probability-based method for representing and reasoning with uncertain knowledge. This work deals with constraints in BNs and investigates how tournament selection can be adapted to better process such constraints in the context of abductive inference. Abductive inference in BNs consists of finding the most probable explanation given some evidence. Since exact abductive inference is NP-hard, several approximate approaches to this inference task have been developed. One of them applies evolutionary techniques in order to find optimal or close-to-optimal explanations. A problem with the traditional evolutionary approach is this: As the number of constraints determined by the zeros in the conditional probability tables grows, performance deteriorates because the number of explanations whose probability is greater than zero decreases. To minimize this problem, this paper presents and analyzes a new evolutionary approach to abductive inference in BNs. By considering abductive inference as a constraint optimization problem, the novel approach improves performance dramatically when a BN's conditional probability tables contain a significant number of zeros. Experimental results are presented comparing the performances of the traditional evolutionary approach and the approach introduced in this work. The results show that the new approach significantly outperforms the traditional one.