Empirical modeling using symbolic regression via postfix Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Dabhi:2011:ICIIP,

• author = "Vipul K. Dabhi and Sanjay K. Vij",
• title = "Empirical modeling using symbolic regression via postfix Genetic Programming",
• booktitle = "International Conference on Image Information Processing (ICIIP 2011)",
• year = "2011",
• month = "3-5 " # nov,
• address = "Himachal Pradesh",
• size = "6 pages",
• abstract = "Developing mathematical model of a process or system from experimental data is known as empirical modelling. Traditional mathematical techniques are unsuitable to solve empirical modelling problems due to their nonlinearity and multimodality. So, there is a need of an artificial expert that can create model from experimental data. In this paper, we explored the suitability of Neural Network (NN) and symbolic regression via Genetic Programming (GP) to solve empirical modelling problems and conclude that symbolic regression via GP can deal efficiently with these problems. This paper aims to introduce a novel GP approach to symbolic regression for solving empirical modelling problems. The main contribution includes: (i) a new method of chromosome representation (postfix based) and evaluation (stack based) to reduce space-time complexity of algorithm (ii) comparison of our approach with Gene Expression Programming (GEP), a GP variant (iii) algorithms for generating valid chromosomes (in postfix notation) and identifying non-coding region of chromosome to improve efficiency of evolutionary process. Experimental results showed that empirical modelling problems can be solved efficiently using symbolic regression via postfix GP approach.",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, chromosome evaluation, chromosome representation, empirical modelling problem, evolutionary process, gene expression programming, neural network, postfix genetic programming, space-time complexity reduction, symbolic regression, computational complexity, modelling, neural nets",
• DOI = "doi:10.1109/ICIIP.2011.6108857",
• notes = "Also known as \cite{6108857}",

}

Genetic Programming entries for Vipul K Dabhi Sanjay K Vij

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