Dew Point modelling using GEP based multi objective optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{oai:arXiv.org:1304.5594,
  author =       "Siddharth Shroff and Vipul Dabhi",
  title =        "Dew Point modelling using {GEP} based multi objective
                 optimization",
  note =         "Comment: 14 pages, 8 figures. arXiv admin note:
                 substantial text overlap with arXiv:1304.4055",
  howpublished = "arXiv",
  year =         "2013",
  month =        apr # "~23",
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1304.5594",
  URL =          "http://arxiv.org/abs/1304.5594",
  size =         "14 pages",
  abstract =     "Different techniques are used to model the
                 relationship between temperatures, dew point and
                 relative humidity. Gene expression programming is
                 capable of modelling complex realities with great
                 accuracy, allowing at the same time, the extraction of
                 knowledge from the evolved models compared to other
                 learning algorithms. We aim to use Gene Expression
                 Programming for modelling of dew point. Generally,
                 accuracy of the model is the only objective used by
                 selection mechanism of GEP. This will evolve large size
                 models with low training error. To avoid this
                 situation, use of multiple objectives, like accuracy
                 and size of the model are preferred by Genetic
                 Programming practitioners. Solution to a
                 multi-objective problem is a set of solutions which
                 satisfies the objectives given by decision maker. Multi
                 objective based GEP will be used to evolve simple
                 models. Various algorithms widely used for multi
                 objective optimisation, like NSGA II and SPEA 2, are
                 tested on different test problems. The results obtained
                 thereafter gives idea that SPEA 2 is better than NSGA
                 II based on the features like execution time, number of
                 solutions obtained and convergence rate. We selected
                 SPEA 2 for dew point prediction. The multi-objective
                 base GEP produces accurate and simpler (smaller)
                 solutions compared to solutions produced by plain GEP
                 for dew point predictions. Thus multi objective base
                 GEP produces better solutions by considering the dual
                 objectives of fitness and size of the solution. These
                 simple models can be used to predict future values of
                 dew point.",
}

Genetic Programming entries for Siddharth Shroff Vipul K Dabhi

Citations