Modified Neldermead Simplex Method for Clustering Gene Expression Data

In many numerical tests, Nelder-Mead (NM) method succeeds in obtaining a good reduction in the function value using a relatively small number of function evaluations. Generally, the microarray gene expression data dimension is high. However, when optimizing high dimensional problems, Nelder-Mead method suffers from poor convergence rate and early restart. To overcome this problem and to increase the global search area, the Modified Nelder-Mead (MNM) is proposed. In the proposed work, the expansion step is replaced by a new step called spread out. It is processed based on the assumption that a better point will be available apart from the best and good point. This may increase the global search considerably and will result in a better solution.

Autorentext

Dr M Pandi is currently working as an Associate Professor in the Department of CSE at Bharat Institute of Engineering and Technology, Hyderabad, India. He received his Ph.D (CSE), M.E. (Software Engineering) and B.Tech (Information Technology) from Anna University, Chennai. His areas of interest include data mining and optimization techniques.