Jadavpur University

About: Jadavpur University is a education organization based out in Kolkata, India. It is known for research contribution in the topics: Population & Schiff base. The organization has 10856 authors who have published 27678 publications receiving 422069 citations. The organization is also known as: JU & Jadabpur University.

Recursive Memetic Algorithm for gene selection in microarray data

Abstract: Feature selection algorithm contributes a lot in the domain of medical diagnosis. Choosing a small subset of genes that enable a classifier to predict the presence or type of disease accurately is a difficult optimisation problem due to the size of the microarray data. The dual task of achieving higher accuracy and a small number of features makes it a challenging research problem. In our work, we have developed a Recursive Memetic Algorithm (RMA) model for selection of genes. It is a variant of Memetic Algorithm (MA) and performs much better than MA as well as Genetic Algorithm (GA). RMA has been applied on seven microarray datasets namely, AMLGSE2191, Colon, DLBCL, Leukaemia, Prostate, MLL and SRBCT. Encouraging results obtained by the proposed model, reported in this article, are biologically validated with the use of Gene Oncology, KEGG pathways and heat maps.

Improving particle swarm optimization with differentially perturbed velocity

Abstract: This paper introduces a novel scheme of improving the performance of particle swarm optimization (PSO) by a vector differential operator borrowed from differential evolution (DE). Performance comparisons of the proposed method are provided against (a) the original DE, (b) the canonical PSO, and (c) three recent, high-performance PSO-variants. The new algorithm is shown to be statistically significantly better on a seven-function test suite for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability.

Statistical Methods in QSAR/QSPR

Abstract: QSAR/QSPR studies are aimed at developing correlation models using a response of chemicals (activity/property) and chemical information data in a statistical approach. The regression- and classification-based strategies are employed to serve the purpose of developing models for quantitative and graded response data, respectively. In addition to the conventional methods, various machine learning tools are also useful for QSAR/QSPR modeling analysis especially for studies involving high-dimensional and complex chemical information data bearing a nonlinear relationship with the response under consideration.

Combined Heat and Power Economic Dispatch by Using Differential Evolution

Abstract: This article presents differential evolution to solve the combined heat and power economic dispatch problem. Differential evolution, an improved version of the genetic algorithm and evolutionary programming, is a very simple, fast, and robust global optimization technique. The proposed algorithm is illustrated for a test system, and the test results are compared with those obtained from particle swarm optimization and evolutionary programming.