C4.5: programs for machine learning
C4.5: programs for machine learning
Competition-Based Induction of Decision Models from Examples
Machine Learning - Special issue on genetic algorithms
Data transformations for eliminating conflict misses
PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Machine Learning
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
Green Supercomputing Comes of Age
IT Professional
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
Parallel Computing Experiences with CUDA
IEEE Micro
Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation
Engineering Applications of Artificial Intelligence
Editorial: Hybrid learning machines
Neurocomputing
Performance and efficiency of memetic pittsburgh learning classifier systems
Evolutionary Computation
Fuzzy-UCS: a Michigan-style learning fuzzy-classifier system for supervised learning
IEEE Transactions on Evolutionary Computation
Logic-oriented neural networks for fuzzy neurocomputing
Neurocomputing
Information Sciences: an International Journal
A SIMD interpreter for genetic programming on GPU graphics cards
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
Speeding up the evaluation of evolutionary learning systems using GPGPUs
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Efficient Distributed Genetic Algorithm for Rule extraction
Applied Soft Computing
Large scale data mining using genetics-based machine learning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A parallel genetic programming algorithm for classification
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
An efficient evolutionary scheduling algorithm for parallel job model in grid environment
PaCT'11 Proceedings of the 11th international conference on Parallel computing technologies
Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Fuzzy Systems
Speeding up the evaluation phase of GP classification algorithms on GPUs
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
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
Natural Encoding for Evolutionary Supervised Learning
IEEE Transactions on Evolutionary Computation
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
Fuzzy-XCS: A Michigan Genetic Fuzzy System
IEEE Transactions on Fuzzy Systems
Accelerated parallel genetic programming tree evaluation with OpenCL
Journal of Parallel and Distributed Computing
The Journal of Supercomputing
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Individuals from Pittsburgh rule-based classifiers represent a complete solution to the classification problem and each individual is a variable-length set of rules. Therefore, these systems usually demand a high level of computational resources and run-time, which increases as the complexity and the size of the data sets. It is known that this computational cost is mainly due to the recurring evaluation process of the rules and the individuals as rule sets. In this paper we propose a parallel evaluation model of rules and rule sets on GPUs based on the NVIDIA CUDA programming model which significantly allows reducing the run-time and speeding up the algorithm. The results obtained from the experimental study support the great efficiency and high performance of the GPU model, which is scalable to multiple GPU devices. The GPU model achieves a rule interpreter performance of up to 64 billion operations per second and the evaluation of the individuals is speeded up of up to 3.461xwhen compared to the CPU model. This provides a significant advantage of the GPU model, especially addressing large and complex problems within reasonable time, where the CPU run-time is not acceptable.