Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
ECSQAU Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Bayesian approach to sensor-based context awareness
Personal and Ubiquitous Computing
A Method Based on Genetic Algorithms and Fuzzy Logic to Induce Bayesian Networks
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A method for evaluating elicitation schemes for probabilisticmodels
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
A new range-free localization method using quadratic programming
Computer Communications
Grammar-guided evolutionary construction of bayesian networks
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
A review on evolutionary algorithms in Bayesian network learning and inference tasks
Information Sciences: an International Journal
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A new structure learning approach for Bayesian networks (BNs) based on dual genetic algorithm (DGA) is proposed in this paper. An individual of the population is represented as a dual chromosome composed of two chromosomes. The first chromosome represents the ordering among the BN nodes and the second represents the conditional dependencies among the ordered BN nodes. It is rigorously shown that there is no BN structure that cannot be encoded by the proposed dual genetic encoding and the proposed encoding explores the entire solution space of the BN structures. In contrast with existing GA-based structure learning methods, the proposed method learns not only the topology of the BN nodes, but also the ordering among the BN nodes, thereby, exploring the wider solution space of a given problem than the existing method. The dual genetic operators are closed in the set of the admissible individuals. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulation.