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The Tabu_Genetic Algorithm: A Novel Method for Hyper-Parameter Optimization of Learning Algorithms

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School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
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Heavy-duty Intelligent Manufacturing Equipment Innovation Center of Hebei Province, Qinhuangdao 066004, China
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Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
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Author to whom correspondence should be addressed.
Electronics 2019, 8(5), 579; https://doi.org/10.3390/electronics8050579
Received: 30 April 2019 / Revised: 22 May 2019 / Accepted: 24 May 2019 / Published: 25 May 2019
(This article belongs to the Section Artificial Intelligence)
Machine learning algorithms have been widely used to deal with a variety of practical problems such as computer vision and speech processing. But the performance of machine learning algorithms is primarily affected by their hyper-parameters, as without good hyper-parameter values the performance of these algorithms will be very poor. Unfortunately, for complex machine learning models like deep neural networks, it is very difficult to determine their hyper-parameters. Therefore, it is of great significance to develop an efficient algorithm for hyper-parameter automatic optimization. In this paper, a novel hyper-parameter optimization methodology is presented to combine the advantages of a Genetic Algorithm and Tabu Search to achieve the efficient search for hyper-parameters of learning algorithms. This method is defined as the Tabu_Genetic Algorithm. In order to verify the performance of the proposed algorithm, two sets of contrast experiments are conducted. The Tabu_Genetic Algorithm and other four methods are simultaneously used to search for good values of hyper-parameters of deep convolutional neural networks. Experimental results show that, compared to Random Search and Bayesian optimization methods, the proposed Tabu_Genetic Algorithm finds a better model in less time. Whether in a low-dimensional or high-dimensional space, the Tabu_Genetic Algorithm has better search capabilities as an effective method for finding the hyper-parameters of learning algorithms. The presented method in this paper provides a new solution for solving the hyper-parameters optimization problem of complex machine learning models, which will provide machine learning algorithms with better performance when solving practical problems. View Full-Text
Keywords: genetic algorithms; machine learning algorithms; neural networks; optimization methods; hyper-parameter optimization genetic algorithms; machine learning algorithms; neural networks; optimization methods; hyper-parameter optimization
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Guo, B.; Hu, J.; Wu, W.; Peng, Q.; Wu, F. The Tabu_Genetic Algorithm: A Novel Method for Hyper-Parameter Optimization of Learning Algorithms. Electronics 2019, 8, 579.

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