Data-driven paradigms of EvoNN and BioGP
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Chakraborti:2015:csdc,
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author = "Nirupam Chakraborti",
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title = "Data-driven paradigms of {EvoNN} and {BioGP}",
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booktitle = "Complex Systems Digital Campus E-conference,
CS-DC'15",
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year = "2015",
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editor = "Paul Bourgine and Pierre Collet",
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pages = "Paper ID: 356",
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month = sep # " 30-" # oct # " 1",
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note = "Invited talk",
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keywords = "genetic algorithms, genetic programming, ANN",
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URL = "http://cs-dc-15.org/",
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URL = "http://cs-dc-15.org/papers/multi-scale-dynamics/evol-comp-methods-2/data-driven-paradigms-of-evonn-and-biogp/",
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video_url = "http://bbb.univ-paris8.fr/playback/presentation/0.9.0/playback.html?meetingId=abfae475e9e5adf03d2df42d7d34f47e8e173fdc-1443413857859",
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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.",
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notes = "1 October 2015 5:40 to 6:10 (UTC) Evolutionary
Computing Methods session
Does not appear in proceedings published by Springer
2017
Video working Dec 2019
http://bbb.univ-paris8.fr/playback/presentation/0.9.0/playback.html?meetingId=abfae475e9e5adf03d2df42d7d34f47e8e173fdc-1443413857859",
- }
Genetic Programming entries for
Nirupam Chakraborti
Citations