Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Proceedings of the European Conference on Genetic Programming
Automatic Parallelization of Arbitrary Programs
Proceedings of the Second European Workshop on Genetic Programming
Validity of the single processor approach to achieving large scale computing capabilities
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Paragen: a novel technique for the autoparallelisation of sequential programs using GP
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Bio-inspired simulation tool for PERT
Proceedings of the 2009 International Conference on Hybrid Information Technology
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A method is presented for evolving individuals that use an Attribute Grammar (AG) in a generative way. AGs are considerably more flexible and powerful than the closed, context free grammars normally employed by GP. Rather than evolving derivation trees as in most approaches, we employ a two step process that first generates a vector of real numbers using standard GP, before using the vector to produce a parse tree. As the parse tree is being produced, the choices in the grammar depend on the attributes being input to the current node of the parse tree. The motivation is automatic parallelization or the discovery of a re-factoring of a sequential code or equivalent parallel code that satisfies certain performance gains when implemented on a target parallel computing platform such as a multicore processor. An illustrative and a computed example demonstrate this methodology.