Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain

Created by W.Langdon from gp-bibliography.bib Revision:1.8194

@InCollection{Chakraborti:2016:camdtpa,

• author = "Nirupam Chakraborti",
• title = "Data-Driven Bi-Objective Genetic Algorithms EvoNN and BioGP and Their Applications in Metallurgical and Materials Domain",
• booktitle = "Computational Approaches to Materials Design: Theoretical and Practical Aspects",
• publisher = "IGI Global",
• year = "2016",
• editor = "Shubhabrata Datta and J. Paulo Davim",
• chapter = "12",
• pages = "346--368",
• month = jun,
• keywords = "genetic algorithms, genetic programming",
• ISBN = "9781-5225-0290-6",
• DOI = "doi:10.4018/978-1-5225-0290-6.ch012",
• abstract = "Data-driven modeling and optimization are now of utmost importance in computational materials research. This chapter presents the operational details of two recent algorithms EvoNN (Evolutionary Neural net) and BioGP (Bi-objective Genetic Programming) which are particularly suitable for modeling and optimization tasks pertinent to noisy data. In both the approaches a tradeoff between the accuracy and complexity of the candidate models are sought, ultimately leading to some optimum tradeoffs. These novel strategies are tailor-made for constructing models of right complexity, excluding the non-essential inputs. They are constructed to implement the notion of Pareto-optimality using a predator-prey type genetic algorithm, providing the user with a set of optimum models, out of which an appropriate one can be easily picked up by applying some external criteria, if necessary. Several materials related problems have been solved using these algorithms in recent times and a couple of typical examples are briefly presented",

}

Genetic Programming entries for Nirupam Chakraborti

Citations