12452
Vasileios Kolonias, George Goulas, Christos Gogos, Panayiotis Alefragis, Efthymios Housos
The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. […]
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Adam Call
The idea behind this project was to create a simulation of the evolution of life in CUDA. In this simulation the creatures are individual entities that can interact with the world. Each has its own set of state information and DNA representing it. Through this DNA the creatures evolve via division and mating. The evolution […]
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W. B. Langdon, M. Harman
Genetic Programming (GP) may dramatically increase the performance of software written by domain experts. GP and autotuning are used to optimise and refactor legacy GPGPU C code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards are reported compared to the original kernel on the […]
Vuppuluri Sumati
This paper deals about the parallel implementation of the compact Genetic Algorithm on the Compute Unified Device Architecture (CUDA) platform of GPU. We elaborate implementation details on the parallel platform.
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Youssef S.G. Nashed, Roberto Ugolotti, Pablo Mesejo, Stefano Cagnoni
Evolutionary Computation techniques and other metaheuristics have been increasingly used in the last years for solving many real-world tasks that can be formulated as optimization problems. Among their numerous strengths, a major one is their natural predisposition to parallelization. In this paper, we introduce libCudaOptimize, an open source library which implements some metaheuristics for continuous […]
Chung-Yu Shao, Tian-Li Yu
Parallelization is a straightforward approach to enhance the efficiency for evolutionary computation due to its inherently parallel nature. Since NVIDIA released the compute unified device architecture (CUDA), graphic processing units have enabled lots of scalable parallel programs in a wide range of fields. However, parallelization of model building for EDAs is rarely studied. In this […]
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Maria Franco, Natalio Krasnogor, Jaume Bacardit
In this paper we introduce a method for computing fitness in evolutionary learning systems based on NVIDIA’s massive parallel technology using the CUDA library. Both the match process of a population of classifiers against a training set and the computation of the fitness of each classifier from its matches have been parallelized. This method has […]
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Steve Dower
Modern computer processing units tend towards simpler cores in greater numbers, favouring the development of data-parallel applications. Evolutionary algorithms are ideal for taking full advantage of SIMD (Single Instruction, Multiple Data) processing, which is available on both CPUs and GPUs. Creating software that runs on a GPU requires the use of specialised programming languages or […]
V. Krishna Reddy, L.S.S. Reddy
Particle Swarm Optimization (PSO) may be easy but powerful optimization algorithm relying on the social behavior of the particles. PSO has become popular due to its simplicity and its effectiveness in wide range of application with low computational cost. The main objective of this paper is to implement a parallel Asynchronous version and Synchronous versions […]
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Iliana Castro Liera, Marco Antonio Castro Liera, Jesus Antonio Castro
This work presents a parallelization method for the Particle Swarm Optimization algorithm using a low-cost architecture: a General Purpose Graphics Processing Unit (GPGPU). The strategies to better suit the architecture main characteristics are addressed along success rates and convergence times for the optimization of Rastrigin’s and Ackley’s functions on a 30-dimensional search space, and compared […]
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Laurent A. Baumes, Frederic Kruger, Pierre Collet
Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R&D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend […]
Johannes Hofmann
Financial Time Series prediction attempts to model the behavior of financial markets using, among other things, tools like technical, intermarket, and fundamental indicators. Accurate prediction, however, is difficult for a number of reasons: financial markets are influenced, often in a non-linear, sometimes time-lagged fashion, by factors including interest and exchange rates, the rate of economic […]
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