Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
GPGPU: general purpose computation on graphics hardware
ACM SIGGRAPH 2004 Course Notes
The GeForce 6 series GPU architecture
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Parallel genetic algorithms on programmable graphics hardware
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
High performance genetic programming on GPU
BADS '09 Proceedings of the 2009 workshop on Bio-inspired algorithms for distributed systems
Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Parallel multi-objective evolutionary algorithms on graphics processing units
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Genetic programming on graphics processing units
Genetic Programming and Evolvable Machines
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
Hardware accelerators for Cartesian genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms
Genetic Programming and Evolvable Machines
Evolving CUDA PTX programs by quantum inspired linear genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Implementing cartesian genetic programming classifiers on graphics processing units using GPU.NET
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Solving classification problems using genetic programming algorithms on GPUs
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A many threaded CUDA interpreter for genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Fast evaluation of GP trees on GPGPU by optimizing hardware scheduling
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Efficient phenotype evaluation in cartesian genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
Journal of Parallel and Distributed Computing
Hi-index | 0.01 |
In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community aslow cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards.