Abstract The availability of low cost powerful parallel graphics cards has stim-ulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when pro-grammed in the CUDA language. In a first work we have showed that this setup allows to develop fine grain parallelization schemes to evaluate several GP pro-grams in parallel, while obtaining speedups for usual training sets and program sizes. Here we present another parallelization scheme and optimizations about program representation and use of GPU fast memory. This increases the compu-tation speed about three times faster, up to 4 billion GP operations per second. The code has been developed within the well...
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the ...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CU...
The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Pro...
Abstract. The availability of low cost powerful parallel graphics cards has stimulated a trend to po...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Genetic Algorithms(GAs) are suitable for parallel computing since population members fitness maybe e...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
This paper investigates the speed improvements available when using a graphics processing unit (GPU)...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
Računarske metode rješavanja paralelnih problema korištenjem grafičkih obradnih jedinica (GPUs) zadn...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
There are many combinatorial optimization problems such as flow shop scheduling, quadraticassignment...
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the ...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CU...
The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Pro...
Abstract. The availability of low cost powerful parallel graphics cards has stimulated a trend to po...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Genetic Algorithms(GAs) are suitable for parallel computing since population members fitness maybe e...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
This paper investigates the speed improvements available when using a graphics processing unit (GPU)...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
Računarske metode rješavanja paralelnih problema korištenjem grafičkih obradnih jedinica (GPUs) zadn...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Uni...
There are many combinatorial optimization problems such as flow shop scheduling, quadraticassignment...
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the ...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
Trujillo, L., Muñoz Contreras, J. M., Hernandez, D. E., Castelli, M., & Tapia, J. J. (2022). GSGP-CU...