EvoHyp - a Java toolkit for evolutionary algorithm hyper-heuristics

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{pillay:2017:CEC,
  author =       "Nelishia Pillay and Derrick Beckedahl",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "EvoHyp - a Java toolkit for evolutionary algorithm
                 hyper-heuristics",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "2706--2713",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Hyper-heuristics is an emergent technology that has
                 proven to be effective at solving real-world problems.
                 The two main categories of hyper-heuristics are
                 selection and generation. Selection hyper-heuristics
                 select existing low-level heuristics while generation
                 hyper-heuristics create new heuristics. At the
                 inception of the field single point searches were
                 essentially employed by selection hyper-heuristics,
                 however as the field progressed evolutionary algorithms
                 are becoming more prominent. Evolutionary algorithms,
                 namely, genetic programming, have chiefly been used for
                 generation hyper-heuristics. Implementing evolutionary
                 algorithm hyper-heuristics can be quite a
                 time-consuming task which is daunting for first time
                 researchers and practitioners who want to rather focus
                 on the application domain the hyper-heuristic will be
                 applied to which can be quite complex. This paper
                 presents a Java toolkit for the implementation of
                 evolutionary algorithm hyper-heuristics, namely,
                 EvoHyp. EvoHyp includes libraries for a genetic
                 algorithm selection hyper-heuristic (GenAlg), a genetic
                 programming generation hyper-heuristic (GenProg), a
                 distributed version of GenAlg (DistrGenAlg) and a
                 distributed version of GenProg (DistrGenProg). The
                 paper describes the libraries and illustrates how they
                 can be used. The ultimate aim is to provide a toolkit
                 which a non-expert in evolutionary algorithm
                 hyper-heuristics can use. The paper concludes with an
                 overview of future extensions of the toolkit.",
  keywords =     "genetic algorithms, genetic programming, Java,
                 evolutionary computation, DistrGenAlg, DistrGenProg,
                 EvoHyp, Java toolkit, distributed GenAlg, distributed
                 GenProg, evolutionary algorithm hyper-heuristics,
                 generation hyper-heuristics, genetic algorithm
                 selection hyper-heuristic, genetic programming
                 generation hyper-heuristic, low-level heuristics,
                 Biological cells, Libraries, Sociology, Statistics",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969636",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969636}",
}

Genetic Programming entries for Nelishia Pillay Derrick Beckedahl

Citations