Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated
NPC '07 Proceedings of the 2007 IFIP International Conference on Network and Parallel Computing Workshops
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
Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Efficient Parallel Implementation of Evolutionary Algorithms on GPGPU Cards
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Genetic programming on graphics processing units
Genetic Programming and Evolvable Machines
DISPAR-tournament: a parallel population reduction operator that behaves like a tournament
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Parallel genetic algorithm on the CUDA architecture
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Parallel genetic algorithms on programmable graphics hardware
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
EASEA: specification and execution of evolutionary algorithms on GPGPU
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Computation on General Purpose Graphics Processing Units
A many threaded CUDA interpreter for genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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This paper presents two parallelizations of a standard evolutionary algorithm on an NVIDIA GPGPU card, thanks to a parallel replacement operator. These algorithms tackle new problems where previously presented approaches do not obtain satisfactory speedup. If programming is more complicated and fewer options are allowed, the whole algorithm is executed in parallel, thereby fully exploiting the intrinsic parallelism of EAs and the many available GPGPU cores. Finally, the method is validated using two benchmarks.