Computer architecture (2nd ed.): a quantitative approach
Computer architecture (2nd ed.): a quantitative approach
Combating Coevolutionary Disengagement by Reducing Parasite Virulence
Evolutionary Computation
A Monotonic Archive for Pareto-Coevolution
Evolutionary Computation
Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Managing team-based problem solving with symbiotic bid-based genetic programming
Proceedings of the 10th annual conference on Genetic and evolutionary computation
GP on SPMD parallel graphics hardware for mega Bioinformatics data mining
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Linear Genetic Programming
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
Symbiotic coevolutionary genetic programming: a benchmarking study under large attribute spaces
Genetic Programming and Evolvable Machines
A game-theoretic and dynamical-systems analysis of selection methods in coevolution
IEEE Transactions on Evolutionary Computation
A fair comparison of modern CPUs and GPUs running the genetic algorithm under the knapsack benchmark
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
GP under streaming data constraints: a case for pareto archiving?
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A GPU-based implementation of an enhanced GEP algorithm
Proceedings of the 14th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
GPU acceleration of increasingly complex variants of evolutionary frameworks typically assume that all the training data used during evolution resides on the GPU. Such an assumption places limits on the style of application to which evolutionary computation can be applied. Conversely, several coevolutionary frameworks explicitly decouple fitness evaluation from the size of the training partition. Thus, a subset of training exemplars is coevolved with the population of evolved individuals. In this work we articulate the design decisions necessary to support Pareto archiving for Genetic Programming under a commodity GPU platform. Benchmarking of corresponding CPU and GPU implementations demonstrates that the GPU platform is still capable of providing a times ten reduction in computation time.