Affiliations: [a] Hansraj College, University of Delhi, Delhi, India | [b] School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
Correspondence:
[*]
Corresponding author: Manju Sardana, Department of Computer Science, Hansraj College, Mahatma Hansraj Marg, Malkaganj, Delhi 110007, India. Tel.: +91 9968582516; E-mail:[email protected]
Abstract: In order to efficiently explore and exploit large search
space, quantum variant of genetic algorithm has been suggested in
literature. It utilizes quantum computing principle and genetic operators.
Despite the use of the quantum variant of GA, memory and computation time
requirements for high dimensional data like microarrays are huge. In this
paper, we propose a hybrid approach, ClusterQGA, that uses clustering to
select a small set of non-redundant representative genes and then applies
Quantum Genetic Algorithm to determine a minimal set of relevant and
non-redundant genes. Also a new fitness function is proposed to reduce
number of genes without sacrificing the classification accuracy. The
effectiveness of the proposed approach in comparison to existing methods in
terms of classification accuracy and number of features has been
experimentally established for both binary and multi-class publicly
available cancer microarray datasets. The proposed approach reduces the
computation time of Quantum Genetic Algorithm for high dimension microarray
data.
