Brabazon, Anthony

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Brabazon, Anthony
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Brabazon, Anthony

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  • Publication
    GEVA - Grammatical Evolution in Java (v1.0)
    (University College Dublin. School of Computer Science and Informatics, 2008-12-05)
    O'Neill, Michael 
    ;
    Hemberg, Erik 
    ;
    Gilligan, Conor 
    ;
    Bartley, Eliott 
    ;
    McDermott, James 
    ;
    Brabazon, Anthony 
    GEVA is an open source implementation of Grammatical Evolution in Java developed at UCD’s Natural Computing Research & Applications group. As well as providing the characteristic genotype-phenotype mapper of GE a search algorithm engine and a simple GUI are also provided. A number of sample problems and tutorials on how to use and adapt GEVA have been developed.
      100
  • Publication
    Dynamic ant : introducing a new benchmark for genetic programming in dynamic environments
    (University College Dublin. School of Computer Science and Informatics, 2011-04-14)
    Fagan, David 
    ;
    Nicolau, Miguel 
    ;
    Hemberg, Erik 
    ;
    O'Neill, Michael 
    ;
    Brabazon, Anthony 
    In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.
      269
  • Publication
    Dynamic environments can speed up evolution with genetic programming
    (University College Dublin. School of Computer Science and Informatics, 2011)
    O'Neill, Michael 
    ;
    Nicolau, Miguel 
    ;
    Brabazon, Anthony 
    We present a study of dynamic environments with genetic programming to ascertain if a dynamic environment can speed up evolution when compared to an equivalent static environment. We present an analysis of the types of dynamic variation which can occur with a variable-length representation such as adopted in genetic programming identifying modular varying, structural varying and incremental varying goals. An empirical investigation comparing these three types of varying goals on dynamic symbolic regression benchmarks reveals an advantage for goals which vary in terms of increasing structural complexity. This provides evidence to support the added difficulty variable length representations incur due to their requirement to search structural and parametric space concurrently, and how directing search through varying structural goals with increasing complexity can speed up search with genetic programming.
      522