Data-driven paradigms of EvoNN and BioGP

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{Chakraborti:2015:csdc,
  author =       "Nirupam Chakraborti",
  title =        "Data-driven paradigms of EvoNN and BioGP",
  booktitle =    "Complex Systems Digital Campus E-conference,
                 CS-DC'15",
  year =         "2015",
  editor =       "Paul Bourgine and Pierre Collet",
  pages =        "Paper ID: 356",
  month =        sep # " 30-" # oct # " 1",
  note =         "Invited talk",
  keywords =     "genetic algorithms, genetic programming, ANN",
  URL =          "http://cs-dc-15.org/",
  URL =          "http://cs-dc-15.org/papers/multi-scale-dynamics/evol-comp-methods-2/data-driven-paradigms-of-evonn-and-biogp/",
  abstract =     "This paper will present the operational details of two
                 recent algorithms EvoNN (Evolutionary Neural net) and
                 BioGP (Bi-objective Genetic Programming) which are
                 developed for modelling and optimization tasks
                 pertinent to noisy data. EvoNN uses a neural net
                 architecture while BioGP is based upon a tree structure
                 typical of Genetic Programming. A bi-objective Genetic
                 Algorithm acts on a population of either trees or
                 neural nets, seeking a trade-off between the accuracy
                 and complexity of the candidate models, ultimately
                 leading to the optimum models along a Pareto frontier.
                 Both the paradigms are tailor-made for constructing
                 models of right complexity, and in the process of
                 evolution they exclude the non-essential inputs. By
                 default, an optimum model satisfying the Corrected
                 Akaike Information Criterion (AICc) is recommended in
                 case of EvoNN, and for BioGP the optimum model with the
                 minimum training error is recommended. However, a
                 Decision Maker (DM) can select a suitable model from
                 the Pareto frontier by appropriate one can be easily
                 picked up by applying some external criteria, if
                 necessary. Both the algorithms tend to avoid over
                 fitting or under fitting of any noisy data and in case
                 of BioGP special procedures have been implemented to
                 avoid bloat. Any pair of mutually conflicting
                 objectives created through this procedure can also be
                 optimized here using a built-in evolutionary strategy,
                 incorporated as a module.",
  notes =        "1 October 2015 5:40 to 6:10 (UTC) Evolutionary
                 Computing Methods session

                 Does not appear in proceedings published by Springer
                 2017",
}

Genetic Programming entries for Nirupam Chakraborti

Citations