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Regularized machine learning through constraint swarm and evolutionary computation applied to regression problems

Ahmad Mozaffari (Systems Design Engineering Department, University of Waterloo, Waterloo, Canada)
Nasser Lashgarian Azad (Systems Design Engineering Department, University of Waterloo, Waterloo, Canada)
Alireza Fathi (Department of Mechanical Engineering, University of Technology, Babol, Iran)

International Journal of Intelligent Computing and Cybernetics

ISSN: 1756-378X

Article publication date: 4 November 2014




The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).


To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.


Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.


The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.



The authors would like to thank their colleagues at University of California (UC), Berkeley, Professor Karl Hedrick and Dr Pannag Sanketi, for their help in conducting experiments and collecting data for the coldstart modeling problem.


Mozaffari, A., Lashgarian Azad, N. and Fathi, A. (2014), "Regularized machine learning through constraint swarm and evolutionary computation applied to regression problems", International Journal of Intelligent Computing and Cybernetics, Vol. 7 No. 4, pp. 346-381.



Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited

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