- author = "Nirupam Chakraborti",
- title = "Data-Driven Evolutionary Modeling in Materials Technology",
- publisher = "Routledge",
- year = "2023",
- keywords = "genetic algorithms, genetic programming, BioGP, matlab",
- isbn13 = "9781032061733",
-
URL = "
https://www.routledge.com/Data-Driven-Evolutionary-Modeling-in-Materials-Technology/Chakraborti/p/book/9781032061733",
-
DOI = "
doi:10.1201/9781003201045",
- size = "318 pages",
-
abstract = "Chapter 1: Introduction
Chapter 2: Data with random noise and its modeling
Chapter 3: Nature inspired non-calculus optimization
Chapter 4: Single-objective evolutionary algorithms
Chapter 5: Multi-objective evolutionary optimization Chapter 6: Evolutionary learning and optimization using Neural Net paradigm
Chapter 7: Evolutionary learning and optimization using Genetic Programming paradigm Chapter 8: The challenge of big data and Evolutionary Deep Learning
Chapter 9: Software available in public domain and the commercial software
Chapter 10: Applications in Iron and Steel making
Chapter 11: Applications in chemical and metallurgical unit processing
Chapter 12: Applications in Materials Design
Chapter 13: Applications in Atomistic Materials Design
Chapter 14: Applications in Manufacturing
Chapter 15: Miscellaneous Applications",
- notes = "Sections on GP. Review in \cite{Kabliman:2023:GPEM}",
