Professor Nirupam Chakraborti was educated in India and USA, receiving his B.Met.E from Jadavpur University, India, followed by an MS from New Mexico Tech, USA and PhD, PhD degrees from University of Washington, Seattle, USA. He joined Indian Institute of Technology, Kanpur as a member of the faculty in 1984 and switched to Indian Institute of Technology, Kharagpur in 2000.Internationally known for his pioneering work on evolutionary computation in the area of Metallurgy and Materials, globally, Professor Chakraborti was rated among the top 2% highly cited researchers in the Materials area in 2000, as per Scopus records. A former Docent of Åbo Akademi, Finland, former Visiting Professors of Florida International University and POSTECH, Korea, he also taught and conducted research at several other academic institutions in Austria, Brazil, Finland, Germany, Italy and the US. An international symposium, under the KomPlasTech 2019, which is world’s longest running conference series in the area of computational materials technology, was organized in Poland in 2019 to honor him. In 2020, an issue of a prominent Taylor of Francis journal, Materials and Manufacturing Processes was dedicated to him as well. In 2021 Indian Institute of Technology, Kharagpur and Indian Institute of Metals, a professional body, also organized another international seminar in his honor.This book is a culmination of Professor Chakarborti’s decades of research and teaching efforts in this area.

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• 1. Introduction 2. Data with random noise and its modeling3. Nature inspired non-calculus optimization4. Single-objective evolutionary algorithms5. Multi-objective evolutionary optimization 6. Evolutionary learning and optimization using Neural Net paradigm7. Evolutionary learning and optimization using Genetic Programming paradigm 8. The challenge of big data and Evolutionary Deep Learning9. Software available in public domain and the commercial software 10. Applications in Iron and Steel making 11. Applications in chemical and metallurgical unit processing12. Applications in Materials Design13. Applications in Atomistic Materials Design14. Applications in Manufacturing15. Miscellaneous Applications

Professor Nirupam Chakraborti was educated in India and USA, receiving his B.Met.E from Jadavpur University, India, followed by an MS from New Mexico Tech, USA and PhD, PhD degrees from University of Washington, Seattle, USA. He joined Indian Institute of Technology, Kanpur as a member of the faculty in 1984 and switched to Indian Institute of Technology, Kharagpur in 2000.Internationally known for his pioneering work on evolutionary computation in the area of Metallurgy and Materials, globally, Professor Chakraborti was rated among the top 2% highly cited researchers in the Materials area in 2000, as per Scopus records. A former Docent of Åbo Akademi, Finland, former Visiting Professors of Florida International University and POSTECH, Korea, he also taught and conducted research at several other academic institutions in Austria, Brazil, Finland, Germany, Italy and the US. An international symposium, under the KomPlasTech 2019, which is world’s longest running conference series in the area of computational materials technology, was organized in Poland in 2019 to honor him. In 2020, an issue of a prominent Taylor of Francis journal, Materials and Manufacturing Processes was dedicated to him as well. In 2021 Indian Institute of Technology, Kharagpur and Indian Institute of Metals, a professional body, also organized another international seminar in his honor.This book is a culmination of Professor Chakarborti’s decades of research and teaching efforts in this area.

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• 1. Introduction 2. Data with random noise and its modeling3. Nature inspired non-calculus optimization4. Single-objective evolutionary algorithms5. Multi-objective evolutionary optimization 6. Evolutionary learning and optimization using Neural Net paradigm7. Evolutionary learning and optimization using Genetic Programming paradigm 8. The challenge of big data and Evolutionary Deep Learning9. Software available in public domain and the commercial software 10. Applications in Iron and Steel making 11. Applications in chemical and metallurgical unit processing12. Applications in Materials Design13. Applications in Atomistic Materials Design14. Applications in Manufacturing15. Miscellaneous Applications

Professor Nirupam Chakraborti was educated in India and USA, receiving his B.Met.E from Jadavpur University, India, followed by an MS from New Mexico Tech, USA and PhD, PhD degrees from University of Washington, Seattle, USA. He joined Indian Institute of Technology, Kanpur as a member of the faculty in 1984 and switched to Indian Institute of Technology, Kharagpur in 2000.Internationally known for his pioneering work on evolutionary computation in the area of Metallurgy and Materials, globally, Professor Chakraborti was rated among the top 2% highly cited researchers in the Materials area in 2000, as per Scopus records. A former Docent of Åbo Akademi, Finland, former Visiting Professors of Florida International University and POSTECH, Korea, he also taught and conducted research at several other academic institutions in Austria, Brazil, Finland, Germany, Italy and the US. An international symposium, under the KomPlasTech 2019, which is world’s longest running conference series in the area of computational materials technology, was organized in Poland in 2019 to honor him. In 2020, an issue of a prominent Taylor of Francis journal, Materials and Manufacturing Processes was dedicated to him as well. In 2021 Indian Institute of Technology, Kharagpur and Indian Institute of Metals, a professional body, also organized another international seminar in his honor.This book is a culmination of Professor Chakarborti’s decades of research and teaching efforts in this area.

Select Guide Rating

• 1. Introduction 2. Data with random noise and its modeling3. Nature inspired non-calculus optimization4. Single-objective evolutionary algorithms5. Multi-objective evolutionary optimization 6. Evolutionary learning and optimization using Neural Net paradigm7. Evolutionary learning and optimization using Genetic Programming paradigm 8. The challenge of big data and Evolutionary Deep Learning9. Software available in public domain and the commercial software 10. Applications in Iron and Steel making 11. Applications in chemical and metallurgical unit processing12. Applications in Materials Design13. Applications in Atomistic Materials Design14. Applications in Manufacturing15. Miscellaneous Applications