International audienceThe island model for evolutionary algorithms allows to delay the global convergence of the evolution process and encourage diversity. However, solving large size and time-intensive combinatorial optimization problems with the island model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, we focus on the parallel island model on GPU. We address its re-design, implementation, and associated issues related to the GPU execution context. The preliminary results demonstrate the effectiveness of the proposed approaches and their capabilities to fully exploit the GPU architecture
GPGPUs offer significant computational power for programmers to leverage. This computational power i...
Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems s...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
International audienceThe island model for evolutionary algorithms allows to delay the global conver...
This paper presents a parallel approach of the genetic algorithm (GA) over the Graphical Processing ...
International audienceA parallel solution to the implementation of evolutionary algorithms is propos...
In this research, we have implemented a parallel EP on consumer-level graphics processing units and ...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
Graphical Processing Units stand for the success of Artificial Neural Networks over the past decade ...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
International audienceOver the last years, interest in hybrid metaheuristics has risen considerably ...
Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good...
Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing ...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
GPGPUs offer significant computational power for programmers to leverage. This computational power i...
Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems s...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...
International audienceThe island model for evolutionary algorithms allows to delay the global conver...
This paper presents a parallel approach of the genetic algorithm (GA) over the Graphical Processing ...
International audienceA parallel solution to the implementation of evolutionary algorithms is propos...
In this research, we have implemented a parallel EP on consumer-level graphics processing units and ...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
Graphical Processing Units stand for the success of Artificial Neural Networks over the past decade ...
This paper propose a multithreaded Genetic Programming classi cation evaluation model using NVIDIA...
International audienceOver the last years, interest in hybrid metaheuristics has risen considerably ...
Facility layout problem (FLP) is one of the hottest research areas in industrial engineering. A good...
Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing ...
Parallel genetic algorithms, models and implementations, attempts to exploit the intrinsically paral...
Evolutionary algorithms (EA) are proven effective and robust in searching large varied spaces in a w...
GPGPUs offer significant computational power for programmers to leverage. This computational power i...
Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems s...
Genetic programming (GP) is a machine learning technique that is based on the evolution of computer ...