This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster. The proposed implementation employs an island-based genetic algorithm where every GPU evolves a single island. The individuals are processed by CUDA warps, which enables the solution of large knapsack instances and eliminates undesirable thread divergence. The MPI interface is used to exchange genetic material among isolated islands and collect statistical data. The characteristics of the proposed GAs are investigated on a two-node cluster composed of 14 Fermi GPUs and 4 six-core Intel Xeon processors. The overall GPU performance of the proposed GA reaches 5.67 TFLOPS
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Abstract Detecting epistasis, such as 2-SNP interac-tions, in Genome-Wide Association Studies (GWAS)...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel comp...
This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster...
A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic al...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
Abstract. This paper deals with the mapping of the parallel island-based genetic algorithm with unid...
This paper presents a parallel approach of the genetic algorithm (GA) over the Graphical Processing ...
Genetic Algorithm GA has emerged as a powerful tool to discover optimal for multidimensional knapsac...
Feature or variable selection still remains an unsolved problem, due to the infeasible evaluation of...
The knapsack problem manifests itself in many domains like cryptography, financial domain and bio-in...
International audienceIn this article, we propose a parallel implementation of the dynamic programmi...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
International audienceThe island model for evolutionary algorithms allows to delay the global conver...
AbstractThe Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Proble...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Abstract Detecting epistasis, such as 2-SNP interac-tions, in Genome-Wide Association Studies (GWAS)...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel comp...
This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster...
A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic al...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel co...
Abstract. This paper deals with the mapping of the parallel island-based genetic algorithm with unid...
This paper presents a parallel approach of the genetic algorithm (GA) over the Graphical Processing ...
Genetic Algorithm GA has emerged as a powerful tool to discover optimal for multidimensional knapsac...
Feature or variable selection still remains an unsolved problem, due to the infeasible evaluation of...
The knapsack problem manifests itself in many domains like cryptography, financial domain and bio-in...
International audienceIn this article, we propose a parallel implementation of the dynamic programmi...
This thesis represents master's thesis focused on acceleration of Genetic algorithms using GPU. Firs...
International audienceThe island model for evolutionary algorithms allows to delay the global conver...
AbstractThe Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Proble...
Genetic Algorithms (GAs) is proven to be effective in solving many optimization tasks. GAs is one of...
Abstract Detecting epistasis, such as 2-SNP interac-tions, in Genome-Wide Association Studies (GWAS)...
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel comp...