Hybridization of Genetic and Quantum Algorithm for gene selection and classification of Microarray data

In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional Microarray Data. We named our algorithm GQASVM. Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy.

[1]  Jong-Hwan Kim,et al.  Parallel quantum-inspired genetic algorithm for combinatorial optimization problem , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[2]  Jong-Hwan Kim,et al.  Genetic quantum algorithm and its application to combinatorial optimization problem , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[3]  M. Batouche,et al.  A new quantum-inspired genetic algorithm for solving the travelling salesman problem , 2004, 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT '04..

[4]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.

[5]  Lov K. Grover A fast quantum mechanical algorithm for database search , 1996, STOC '96.

[6]  El-Ghazali Talbi,et al.  ParadisEO: A Framework for Parallel and Distributed Metaheuristics , 2003 .

[7]  Nello Cristianini,et al.  Support vector machine classification and validation of cancer tissue samples using microarray expression data , 2000, Bioinform..

[8]  P. Shor Doc. Math. J. Dmv 1 Quantum Computing , 1998 .

[9]  Shutao Li,et al.  A Support Vector Machine Ensemble for Cancer Classification Using Gene Expression Data , 2007, ISBRA.

[10]  M. Ng,et al.  Informative Gene Discovery for Cancer Classification from Microarray Expression Data , 2005, 2005 IEEE Workshop on Machine Learning for Signal Processing.

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  Huan Liu,et al.  Redundancy based feature selection for microarray data , 2004, KDD.

[13]  Charles H. Bennett,et al.  Teleporting an unknown quantum state via dual classical and Einstein-Podolsky-Rosen channels. , 1993, Physical review letters.

[14]  Ed Keedwell,et al.  Two-Phase EA/k-NN for Feature Selection and Classification in Cancer Microarray Datasets , 2005, 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[15]  El-Ghazali Talbi,et al.  A comparison of PSO and GA approaches for gene selection and classification of microarray data , 2007, GECCO '07.

[16]  Gilles Brassard,et al.  Experimental Quantum Cryptography , 1990, EUROCRYPT.

[17]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[18]  Jin-Kao Hao,et al.  A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data , 2006, EvoWorkshops.

[19]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Feature Subset Selection , 1977, IEEE Transactions on Computers.

[20]  William Stafford Noble,et al.  Support vector machine classification on the web , 2004, Bioinform..

[21]  Constantin F. Aliferis,et al.  Methods for Multi-Category Cancer Diagnosis from Gene Expression Data: A Comprehensive Evaluation to Inform Decision Support System Development , 2004, MedInfo.

[22]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[23]  Tianzi Jiang,et al.  A combinational feature selection and ensemble neural network method for classification of gene expression data , 2004, BMC Bioinformatics.

[24]  Bþ KHI,et al.  Classification of Two-Class Cancer Data Reliably Using , .

[25]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.