Communications of the ACM - Special issue on parallelism
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Massively parallel genetic programming
Advances in genetic programming
Symbolic Regression via Genetic Programming
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
IEEE Annals of the History of Computing
Fast Genetic Programming and Artificial Developmental Systems on GPUs
HPCS '07 Proceedings of the 21st International Symposium on High Performance Computing Systems and Applications
Green Supercomputing Comes of Age
IT Professional
Introduction to the cell broadband engine architecture
IBM Journal of Research and Development
High performance genetic programming on GPU
BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
Genetic programming on graphics processing units
Genetic Programming and Evolvable Machines
A parallel implementation of genetic programming that achieves super-linear performance
Information Sciences: an International Journal
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
A SIMD interpreter for genetic programming on GPU graphics cards
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Population parallel GP on the G80 GPU
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Understanding throughput-oriented architectures
Communications of the ACM
Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Heterogeneous Computing with OpenCL
Heterogeneous Computing with OpenCL
OpenCL Programming Guide
Parallel genetic algorithms on programmable graphics hardware
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Fast evaluation of GP trees on GPGPU by optimizing hardware scheduling
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Short communication: A framework for automating the configuration of OpenCL
Environmental Modelling & Software
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Inspired by the process of natural selection, genetic programming (GP) aims at automatically building arbitrarily complex computer programs. Being classified as an ''embarrassingly'' parallel technique, GP can theoretically scale up to tackle very diverse problems by increasingly adding computational power to its arsenal. With today's availability of many powerful parallel architectures, a challenge is to take advantage of all those heterogeneous compute devices in a portable and uniform way. This work proposes both (i) a transcription of existing GP parallelization strategies into the OpenCL programming platform; and (ii) a freely available implementation to evaluate its suitability for GP, by assessing the performance of parallel strategies on the CPU and GPU processors from different vendors. Benchmarks on the symbolic regression and data classification domains were performed. On the GPU we could achieve 13 billion node evaluations per second, delivering almost 10 times the throughput of a twelve-core CPU.