Optimizing Support Vector Machine Parameters based on Quantum and Immune Algorithm

doi:10.23940/ijpe.19.03.p8.792802

Abstract

Abstract: In view of premature convergence and blind searching of the quantum and immune algorithm in the evolution process, this paper proposes two improvements. Firstly, the fitness function is improved by utilizing the mean square error as the fitness function, and the concentration of immune antibodies is introduced to the fitness function to improve the diversity of populations and avoid premature convergence of the algorithm. Secondly, the probability of rotation is adopted to optimize the quantum rotate gate to avoid blind searching and accelerate the convergence of the algorithm. The improved algorithm is adopted to optimize parameters of support vector machines and is applied to network intrusion detection. The experimental results show that the improved algorithm has better optimization effects.

Key words: support vector machine, parameter optimization, quantum computation, quantum immune algorithm

Yuling Tian. Optimizing Support Vector Machine Parameters based on Quantum and Immune Algorithm [J]. Int J Performability Eng, 2019, 15(3): 792-802.

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