Abstract
Learning the Bayesian networks (BNs) structure from data has received increasing attention. Many heuristic algorithms have been introduced to search for the optimal network that best matches the given training data set. To further improve the performance of ant colony optimization (ACO) in learning the BNs structure, this paper proposes a new improved coevolution ACO (coACO) algorithm, which uses the pheromone information as the cooperative factor and the differential evolution (DE) as the cooperative strategy. Different from the basic ACO, the coACO divides the entire ant colony into various sub-colonies (groups), among which DE operators are adopted to implement the cooperative evolutionary process. Experimental results demonstrate that the proposed coACO outperforms the basic ACO in learning the BN structure in terms of convergence and accuracy.
Highlights
The Bayesian network (BN) [1], which is called the probabilistic belief network or the causal network [2], is a kind of graphical model and knowledge representation tool
Two features make the coevolution ACO (coACO) algorithm interesting: (1) the grouping operator divides the entire ant colony into different ant groups, and, the algorithm can carry out the social cooperation between ant individuals and the cooperative interaction and information shared between ant groups; (2) the differential evolution (DE) algorithm is employed to adjust the cooperation information and lead all ant groups to evolve toward the optimum in a cooperative manner
In order to evaluate the performance of the coACO algorithm in solving the BN structure-learning problem, a series of test experiments is performed and a comparison of the proposed coACO with the basic ant colony optimization (ACO) is carried out
Summary
The Bayesian network (BN) [1], which is called the probabilistic belief network or the causal network [2], is a kind of graphical model and knowledge representation tool. BNs can efficiently give the probabilistic description of the dependence or independence relationships between a set of random variables. A BN is composed of a directed acyclic graphical structure and a set of probability parameters. The directed acyclic graphical structure represents the dependence relationships between various variables, and the corresponding probability parameters specify their degree of dependence. Learning the BN structure from a dataset has received increasing attention [3], and researchers have introduced various learning algorithms to obtain the structure for BNs. According to the modeling type [3,4,5], these structure learning algorithms can be classified into methods based on detecting conditional independencies [6,7], known as constraint-based methods, the “score+search”
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