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Nirupam Chakraborti
Researcher at Indian Institute of Technology Kharagpur
Publications - 153
Citations - 3122
Nirupam Chakraborti is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Multi-objective optimization & Genetic algorithm. The author has an hindex of 28, co-authored 146 publications receiving 2813 citations. Previous affiliations of Nirupam Chakraborti include Pohang University of Science and Technology & Indian Institutes of Technology.
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A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
TL;DR: A genetic algorithms based multi-objective optimization technique was utilized in the training process of a feed forward neural network, using noisy data from an industrial iron blast furnace, and a predator-prey algorithm efficiently performed the optimization task.
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Genetic algorithms in materials design and processing
TL;DR: Genetic algorithms (GAs) are biologically inspired computing techniques, which tend to mimic the basic Darwinian concepts of natural selection, and are highly robust and efficient for most engineering optimising studies as mentioned in this paper.
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A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem
TL;DR: In this article, a reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace, and a total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through principal component analysis and optimized simultaneously.
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Genetic programming through bi-objective genetic algorithms with a study of a simulated moving bed process involving multiple objectives
TL;DR: The BioGP technique developed for meta-modeling and applied in a chromatographic separation process using a simulated moving bed (SMB) process produced acceptable results and is now ready for data-driven modeling and optimization studies at large.
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Analyzing Leaching Data for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms
TL;DR: Three distinct cases of leaching in the presence of glucose, sucrose and lactose have been considered and the results compared with an existing analytical model, and the resulting Pareto frontiers are analyzed and discussed.