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.