Evolutionary Data-Driven Modeling

author = "Nirupam Chakraborti",

title = "Evolutionary Data-Driven Modeling",

booktitle = "Informatics for Materials Science and Engineering",

publisher = "Butterworth-Heinemann",

year = "2013",

editor = "Krishna Rajan",

chapter = "5",

pages = "71--95",

address = "Oxford",

keywords = "genetic algorithms, genetic programming, Neural network, Multi-objective optimisation, Evolutionary computation",

isbn13 = "978-0-12-394399-6",

DOI = "

10.1016/B978-0-12-394399-6.00005-9",

URL = "

http://www.sciencedirect.com/science/article/pii/B9780123943996000059",

abstract = "Artificial neural networks (ANNs) and genetic programming (GP) have already emerged as two very effective computing strategies for constructing data-driven models for systems of scientific and engineering interest. However, coming up with accurate models or meta-models from noisy real-life data is often a formidable task due to their frequent association with high degrees of random noise, which might render an ANN or GP model either over- or underfitted. This problem has recently been tackled in two emerging algorithms, Evolutionary Neural Net (EvoNN) and Bi-objective Genetic Programming (BioGP), which use Pareto tradeoff and apply a bi-objective genetic algorithm (GA) in the basic framework of both ANNs and GP.",

notes = "Department of Metallurgical and Materials Engineering, Indian Institute of Technology, Kharagpur, India",