The Use of Local Models Optimized by Genetic Programming Algorithms in Biomedical-Signal Analysis
Created by W.Langdon from
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- @InCollection{Brandejsky:2013:HBO,
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author = "Tomas Brandejsky",
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title = "The Use of Local Models Optimized by Genetic
Programming Algorithms in Biomedical-Signal Analysis",
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booktitle = "Handbook of Optimization",
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publisher = "Springer",
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year = "2013",
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editor = "Ivan Zelinka and Vaclav Snasel and Ajith Abraham",
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volume = "38",
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series = "Intelligent Systems Reference Library",
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chapter = "28",
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pages = "697--716",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-30503-0",
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URL = "http://dx.doi.org/10.1007/978-3-642-30504-7_28",
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DOI = "doi:10.1007/978-3-642-30504-7_28",
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bibdate = "2013-09-25",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/series/isrl/isrl38.html#Brandejsky13",
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URL = "http://dx.doi.org/10.1007/978-3-642-30504-7",
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abstract = "Today researchers need to solve vague defined problems
working with huge data sets describing signals close to
chaotic ones. Common feature of such signals is missing
algebraic model explaining their nature. Genetic
Algorithms and Evolutionary Strategies are suitable to
optimise such models and Genetic Programming Algorithms
to develop them. Hierarchical GPA-ES algorithm
presented herein is used to build compact models of
difficult signals including signals representing
deterministic chaos. Efficiency of GPA-ES is presented
in the paper. Specific group of non-linearly composed
functions similar to real biomedical signals is studied
in the paper. On the base of these prerequisites,
models applicable in complex biomedical signals like
EEG modelling are formed and studied within the
contribution.",
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notes = "CTU in Prague,",
- }
Genetic Programming entries for
Tomas Brandejsky
Citations