This paper propose a multithreaded Genetic Programming classification evaluation model using NVIDIA CUDA GPUs to reduce the computational time due to the poor perfor-mance in large problems. Two different clas-sification algorithms are benchmarked using UCI Machine Learning data sets. Experi-mental results compare the performance us-ing single and multithreaded Java, C and GPU code and show the efficiency far better obtained by our proposal
It is well known that the numerical solution of evolutionary systems and problems based on topologic...
Graphical Processing Units stand for the success of Artificial Neural Networks over the past decade ...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Pro...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
This paper proposes a new approach to produce classification rules based on evolutionary computation...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
Abstract The availability of low cost powerful parallel graphics cards has stim-ulated the port of G...
This paper investigates the speed improvements available when using a graphics processing unit (GPU)...
Genetic Algorithms(GAs) are suitable for parallel computing since population members fitness maybe e...
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the ...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
There are many combinatorial optimization problems such as flow shop scheduling, quadraticassignment...
It is well known that the numerical solution of evolutionary systems and problems based on topologic...
Graphical Processing Units stand for the success of Artificial Neural Networks over the past decade ...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Pro...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
This paper proposes a new approach to produce classification rules based on evolutionary computation...
In this paper, we describe our work to investigate how much cyclic graph based Genetic Programming (...
Abstract The availability of low cost powerful parallel graphics cards has stim-ulated the port of G...
This paper investigates the speed improvements available when using a graphics processing unit (GPU)...
Genetic Algorithms(GAs) are suitable for parallel computing since population members fitness maybe e...
A Single Instruction Multiple Thread CUDA interpreter provides SIMD like parallel evaluation of the ...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
There are many combinatorial optimization problems such as flow shop scheduling, quadraticassignment...
It is well known that the numerical solution of evolutionary systems and problems based on topologic...
Graphical Processing Units stand for the success of Artificial Neural Networks over the past decade ...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...