libCudaOptimize Files

LibCudaOptimize is a GPU-based open source library that allows you to run state-of-the-art bio-inspired optimization heuristics in parallel to optimize a fitness function, introduce a new optimization algorithm, or easily modify/extend existing ones. 
In the first case, the only thing you need to do is to write your fitness to be optimized in C++ or CUDA-C, while in the second and third cases, you can take advantage of the framework offered by the library to avoid the need to go deep into basic implementation issues, especially regarding parallel code.

Methods actually implemented in LibCudaOptimize are:
- Particle Swarm Optimization
- Differential Evolution
- Scatter Search

LibCudaOptimize has been tested on Windows 7 and Ubuntu Linux (from 10.04 to 12.04), using GPUs with CC from 1.3 to 3.0. It works under CUDA 4.X and 5.0 Please notice that GPUs with lower CC impose some limitations to the use of LibCudaOptimize due to hardware limits.

The easiest way to start using LibCudaOptimize is by compiling it using CMake.

LibCudaOptimize is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
LibCudaOptimize is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with LibCudaOptimize. If not, see <http://www.gnu.org/licenses/>.
If you do not agree to these terms, do not download or use this software. For any inquiries different from license information, please contact nashed[at]ce[dot]unipr[dot]it or rob_ugo[at]ce[dot]unipr[dot]it

If you use LibCudaOptimize in your paper, please refer to it as: 
Y.S.G. Nashed, R. Ugolotti, P. Mesejo, S. Cagnoni (2012). "libCudaOptimize: an open source library of GPU-based metaheuristics". In: Proceedings of the 14th Genetic and Evolutionary Computation Conference (companion)