Empirical modeling using symbolic regression via postfix Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  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
  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