Java evolutionary framework based on genetic programming

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@InProceedings{Karasek:2014:SPIN,
  author =       "Jan Karasek and Radim Burget and 
                 Malay Kishore Dutta and Anushikha Singh",
  title =        "Java evolutionary framework based on genetic
                 programming",
  booktitle =    "International Conference on Signal Processing and
                 Integrated Networks (SPIN 2014)",
  year =         "2014",
  month =        feb,
  pages =        "606--612",
  keywords =     "genetic algorithms, genetic programming, hash
                 function, ultra sound image processing, JEF",
  DOI =          "doi:10.1109/SPIN.2014.6777026",
  abstract =     "Automatic optimisation techniques, such as
                 evolutionary algorithms, have become popular in the
                 recent years as a general, simple, robust, and scalable
                 solution which can be applied when other optimisation
                 method fails. Recently, many evolutionary and/or
                 genetic based optimisation frameworks and libraries
                 have been developed and lot of them is freely
                 available. On the other hand, there are not many tools
                 in optimisation field that allows the researchers to
                 implement own code, modify existing code or compare
                 different algorithms. This paper proposes a new grammar
                 driven genetic programming based framework implemented
                 in cross-platform Java programming language which
                 allows to implement own code, modify existing, and
                 analyse algorithms. The framework described in this
                 paper addresses the problem of flexibility, modularity,
                 portability, and presents a general architecture for
                 evolutionary optimisation based on genetic programming
                 driven by context free grammar distributed under the
                 LGPL license suitable for both scientific and business
                 applications. In the paper is described a design of the
                 framework, the motivation for development, and two
                 use-cases.",
  notes =        "Also known as \cite{6777026}",
}

Genetic Programming entries for Jan Karasek Radim Burget Malay Kishore Dutta Anushikha Singh

Citations