Evolutionary Algorithm Using Random Multi-point Crossover Operator for Learning Bayesian Network Structures

Santos, Edimilson Batista dos; Hruschka, Estevam R.; Ebecken, Nelson F. F.

doi:10.1109/icmla.2010.70

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…A new crossover operator, called Random Multi-point Crossover Operator (RMX) [23], is proposed to solve the variable ordering problem. RMX is used for probabilistic graphical models that have directed arcs.…”

Section: Figure 4 Format Of Updated Individualmentioning

confidence: 99%

An Enhanced Multi-Objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks

2021

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In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resource (battery), which requires considerable importance on deployment and routing strategies, to improve the performance of Quality of Service (QoS) in WSNs. Many of the existing strategies are based on metaheuristics algorithms such as Genetic Algorithms to resolve the problem. This research proposes a new algorithm, Enhanced Non-Dominated Sorting Genetic Routing Algorithm (ENSGRA), to improve the QoS in WSNs. The proposed algorithm relies on Non-Dominated Sorting Genetic Algorithm 3 (NSGA-III), but adjusts reference points through the use of a dynamic weighted clustered scheduled vector to obtain new solutions. Moreover, ENSGRA can be used to find an integration between two parents crossover with multi-parent crossover (MPX), to produce multiple children and improve new offspring to obtain the optimal Pareto Fronts (PF). This algorithm excels when compared with the lagged multi-objective jumping particle swarm optimization, Non-dominated Sorting Genetic Algorithm-II and NSGA-III in terms of the QoS model (31% optimization percentage). Results show that the proposed ENSGRA is superior over other algorithms in evaluation measures for multi-objective algorithms.

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Section: Figure 4 Format Of Updated Individualmentioning

confidence: 99%

An Enhanced Multi-Objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks

2021

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“…As for the genetic operators that are utilized during the evolutionary process to produce the offspring, Santos et al developed the random multi-point crossover operator (RMX) [16] and the distance-based mutation operator (DMO) [17] to explore the influence of a single operator on the search for suitable node orderings of BNs. Nevertheless, when a GA searches for the optimal BN structure during the evolutionary process, the crossover operator acting a significant part in the convergence speed is probably ineffective.…”

Section: The Ss Phasementioning

confidence: 99%

“…Two DAGs belong to the same equivalence class if they define the identical conditional independence relations. Another kind of search space is the variable ordering space, which contains all possible permutations of the variables [15][16][17][18]. Since it is much smaller than the DAG space, researchers always consider the search of an optimal variable ordering as an effective way to turn the BN structure learning problem affordable based on the observation that the structure learning of BNs is not NP-hard if given the ordering [15].…”

Section: Introductionmentioning

confidence: 99%

An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

Dai

Ren

Wei

et al. 2018

Neural Comput & Applic

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Learning Bayesian network (BN) structures from data is a NP-hard problem due to the vastness of the solution space. To address this issue, hybrid approaches that integrate the constraint-based (CB) method and the score-and-search (SS) method have been developed in the literature, but when the constrained search space is fixed and inaccurate, it is very likely to lose the optimal solution, leading to low learning accuracy. Besides, due to the randomness and uncertainty of the search, it is difficult to preserve the superiority of the structures, resulting in low learning efficiency. Therefore, we propose a novel hybrid algorithm based on an improved evolutionary approach to explore BN

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“…In the experiments carried out in this work, the new individuals are created using the cross‐over operator random multipoint cross‐over operator (RMX), which was presented in Ref. . This operator has proven to be promising and capable of improving the variable orderings quality.…”

Section: Vomos—variable Ordering Multiple Offspring Samplingmentioning

confidence: 99%

Bayesian Classifiers Applied to the Tennessee Eastman Process

et al. 2013

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Fault diagnosis includes the main task of classification. Bayesian networks (BNs) present several advantages in the classification task, and previous works have suggested their use as classifiers. Because a classifier is often only one part of a larger decision process, this article proposes, for industrial process diagnosis, the use of a Bayesian method called dynamic Markov blanket classifier that has as its main goal the induction of accurate Bayesian classifiers having dependable probability estimates and revealing actual relationships among the most relevant variables. In addition, a new method, named variable ordering multiple offspring sampling capable of inducing a BN to be used as a classifier, is presented. The performance of these methods is assessed on the data of a benchmark problem known as the Tennessee Eastman process. The obtained results are compared with naive Bayes and tree augmented network classifiers, and confirm that both proposed algorithms can provide good classification accuracies as well as knowledge about relevant variables.

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Evolutionary Algorithm Using Random Multi-point Crossover Operator for Learning Bayesian Network Structures

Cited by 8 publications

References 13 publications

An Enhanced Multi-Objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks

An Enhanced Multi-Objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks

An improved evolutionary approach-based hybrid algorithm for Bayesian network structure learning in dynamic constrained search space

Bayesian Classifiers Applied to the Tennessee Eastman Process

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