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Materials and Manufacturing Processes Volume 28, 2013 - Issue 7: Special Issue on Genetic Algorithms
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Original Articles

Designing Cu-Zr Glass Using Multiobjective Genetic Algorithm and Evolutionary Neural Network Metamodels–Based Classical Molecular Dynamics Simulation

Ansul Bansal Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India
,
Ajitesh Barman Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India
,
Sudipto Ghosh Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, IndiaCorrespondence[email protected]
&
Nirupam Chakraborti Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India
Pages 733-740 | Received 27 Aug 2012, Accepted 03 Dec 2012, Published online: 08 Jul 2013

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Dragan Aleksendrić, Costanzo Bellini, Pierpaolo Carlone, Velimir Ćirović, Felice Rubino & Luca Sorrentino. (2019) Neural-fuzzy optimization of thick composites curing process. Materials and Manufacturing Processes 34:3, pages 262-273.
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Umesh Khandey, Sudarsan Ghosh & Krishnaswamy Hariharan. (2017) Machining parameters optimization for satisfying the multiple objectives in machining of MMCs. Materials and Manufacturing Processes 32:10, pages 1082-1093.
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Manwendra K. Tripathi, P. P. Chattopadhyay & Subhas Ganguly. (2017) Evolutionary intelligence in design and synthesis of bulk metallic glasses by mechanical alloying. Materials and Manufacturing Processes 32:10, pages 1059-1066.
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Yiming Li & Chieh-Yang Chen. (2015) Capacitance Characteristic Optimization of Germanium MOSFETs with Aluminum Oxide by Using a Semiconductor-Device-Simulation-Based Multi-Objective Evolutionary Algorithm Method. Materials and Manufacturing Processes 30:4, pages 520-528.
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R. Jha, F. Pettersson, G. S. Dulikravich, H. Saxen & N. Chakraborti. (2015) Evolutionary Design of Nickel-Based Superalloys Using Data-Driven Genetic Algorithms and Related Strategies. Materials and Manufacturing Processes 30:4, pages 488-510.
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Nirupam Chakraborti. (2014) Critical Assessment 3: The unique contributions of multi-objective evolutionary and genetic algorithms in materials research. Materials Science and Technology 30:11, pages 1259-1262.
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H.-J. Jun, K.S. Lee, Y.W. Chang & H.S. Kim. (2014) Finite Element Analysis for Application to the Forming Process of Ti-based Bulk Metallic Glasses. Materials and Manufacturing Processes 29:7, pages 801-807.
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N. Chakraborti. (2014) Promise of multiobjective genetic algorithms in coating performance formulation. Surface Engineering 30:2, pages 79-82.
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Y.C. Li, X.Q. Zhou, M.M. Lu, J.Q. Lin & J.B. Sun. (2014) Parameters Optimization for Machining Optical Parts of Difficult-To-cut Materials by Genetic Algorithm. Materials and Manufacturing Processes 29:1, pages 9-14.
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Shen-Tsu Wang. (2014) Optimized Light Guide Plate Optical Brightness Parameter: Integrating Back-Propagation Neural Network (BPN) and Revised Genetic Algorithm (GA). Materials and Manufacturing Processes 29:1, pages 1-8.
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Articles from other publishers (5)

Robert M. Forrest & A. Lindsay Greer. (2023) Evolutionary design of machine-learning-predicted bulk metallic glasses. Digital Discovery 2:1, pages 202-218.
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Raphael Costa Carvalho, Iasmin Louzada Herzog, Helcio Rangel Barreto Orlande, Marcelo José Colaço, Italo Marcio Madeira & Nirupam Chakraborti. (2023) Parameter estimation with the Markov Chain Monte Carlo method aided by evolutionary neural networks in a water hammer model. Computational and Applied Mathematics 42:1.
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Swagata Roy, Amlan Dutta & Nirupam Chakraborti. (2021) A novel method of determining interatomic potential for Al and Al-Li alloys and studying strength of Al-Al3Li interphase using evolutionary algorithms. Computational Materials Science 190, pages 110258.
Crossref
Pierpaolo Carlone, Dragan Aleksendri & Luca Sorrentino. 2021. Encyclopedia of Materials: Composites. Encyclopedia of Materials: Composites 2 13 .
Nirupam Chakraborti. 2016. Computational Approaches to Materials Design. Computational Approaches to Materials Design 346 368 .

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