An Improved Quantum-Inspired Evolutionary Algorithm for Clustering Gene Expression Data

Zhou, Weixing; Zhou, Cangqi; Liu, G.X.; Lv, Hairong; Liang, Yongquan

doi:10.1007/978-1-4020-3953-9_53

Cited by 5 publications

(1 citation statement)

References 4 publications

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“…The experimental results show that the introduced method achieves equal or shorter average query response times and 3.2-11.3 times faster convergence speed than those of disk allocation methods using CGA. Additionally, bQIEAo was also applied to solve various problems, such as parameter selection for support vector machines (Luo et al 2008), clustering gene expression data (Zhou et al 2006b), neural network training (Ganesh and Singhal 2005) and so on (Lu et al 2008;Liu et al 2005Liu et al , 2006Stojkovic 2006, 2007;Akbarzadeh-T 2005;Xiao et al 2006;Khorsand 2006;Feng et al 2006;Vlachoglannis 2008;Zhao et al 2006;Jang et al 2003Jang et al , 2004bJang et al , 2009Lv and Liu 2007;John and John 2009;Gu et al 2009b;Zhou et al , 2006aLau et al 2009;Araujo et al 2008;Jeong et al 2009;Xing et al 2009aXing et al , 2009bGu et al 2009a). Various problems solved by bQIEAo are summarized in Tables 3 and 4.…”

Section: Bqieaomentioning

confidence: 99%

Quantum-inspired evolutionary algorithms: a survey and empirical study

2010

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Quantum-inspired evolutionary algorithms, one of the three main research areas related to the complex interaction between quantum computing and evolutionary algorithms, are receiving renewed attention. A quantum-inspired evolutionary algorithm is a new evolutionary algorithm for a classical computer rather than for quantum mechanical hardware. This paper provides a unified framework and a comprehensive survey of recent work in this rapidly growing field. After introducing of the main concepts behind quantum-inspired evolutionary algorithms, we present the key ideas related to the multitude of quantum-inspired evolutionary algorithms, sketch the differences between them, survey theoretical developments and applications that range from combinatorial optimizations to numerical optimizations, and compare the advantages and limitations of these various methods. Finally, a small comparative study is conducted to evaluate the performances of different types of quantum-inspired evolutionary algorithms and conclusions are drawn about some of the most promising future research developments in this area.

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