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Bayesian network model structure based on binary evolutionary algorithm

PEERJ COMPUTER SCIENCE 2023年 9卷 e1466页

作者： Yao, Yongna Shangqiu Inst Technol Sch Informat & Elect Engn Shangqiu Peoples R China

With the continuous development of new technologies, the scale of training data is also expanding. Machine learning algorithms are gradually beginning to be studied and applied in places where the scale of data is relatively large. Because the current structure of learning algorithms only focus on the identification of dependencies and ignores the direction of dependencies, it causes multiple labeled samples not to identify categories. Multiple labels need to be classified using techniques such as machine learning and then applied to solve the problem. In the environment of more training data, it is very meaningful to explore the structure extension to identify the dependencies between attributes and take into account the direction of dependencies. In this article, Bayesian network structure learning, analysis of the shortcomings of traditional algorithms, and binary evolutionary algorithm are applied to the randomized algorithm to generate the initial population. In the optimization process of the algorithm, it uses a Bayesian network to do a local search and uses a depth-first algorithm to break the loop. Finally, it finds a higher score for the network structure. In the simulation experiment, the classic data sets, ALARM and INSURANCE, are introduced to verify the effectiveness of the algorithm. Compared with NOTEARS and the Expectation-Maximization (EM) algorithm, the weight evaluation index of this article was 4.5% and 7.3% better than other schemes. The clustering effect was improved by 13.5% and 15.2%. The smallest error and the highest accuracy are also better than other schemes. The discussion of Bayesian reasoning in this article has very important theoretical and practical significance. This article further improves the Bayesian network structure and optimizes the performance of the classifier, which plays a very important role in promoting the expansion of the network structure and provides innovative thinking.

关键词： Network structure Machine learning Bayesian model binary evolutionary algorithm Classifier

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A Comparative Study of Transfer Functions in binary evolutionary algorithms for Single Objective Optimization 15th

A Comparative Study of Transfer Functions in Binary Evolutio...

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15th International Conference on Distributed Computing and Artificial Intelligence (DCAI)

作者： Sawhney, Ramit Shankar, Ravi Jain, Roopal Netaji Subhas Inst Technol Dept Comp Engn New Delhi India

ISBN: (纸本)9783319946498;9783319946481

binary versions of evolutionary algorithms have emerged as alternatives to the state of the art methods for optimization in binary search spaces due to their simplicity and inexpensive computational cost. The adaption of such a binary version from an evolutionary algorithm is based on a transfer function that maps a continuous search space to a discrete search space. In an effort to identify the most efficient combination of transfer functions and algorithms, we investigate binary versions of Gravitational Search, Bat algorithm, and Dragonfly algorithm along with two families of transfer functions in unimodal and multimodal single objective optimization problems. The results indicate that the incorporation of the v-shaped family of transfer functions in the binary Bat algorithm significantly outperforms previous methods in this domain.

关键词： binary evolutionary algorithm Bat algorithm Gravitational search Transfer function Single objective optimization

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