A GPU-Based jDE Algorithm

Population-based search algorithms, such as the Differential Evolution approach, evolve a pool of candidate solutions during the optimization process and are suitable for massively parallel architectures promoted by the use of GPUs. The repository contains a GPU-based implementation of a self-adaptive Differential Evolution employing the jDE mechanism.
Requirements
Compile
$ cd repo
$ make
Parameters Setting
$ "runs, r"      - Number of Executions
$ "pop_size, p"  - Population Size
$ "dim, d"       - Number of Dimensions {2, 5, 10, 20, 30, 50, 100}
$ "func_obj, o"  - Function to Optimize [1, 7]
$ "max_eval, e"  - Number of Function Evaluations
$ "help, h"      - Show this help
Functions Available
Function Name Shifted Rotated Search Space
1 S. Sphere Yes No [-100, +100]
2 S. Rosenbrock Yes No [-100, +100]
3 S. Griewank Yes No [-600, +600]
4 S. Rastrigin Yes No [-5, +5]
5 S. R. Rosenbrock Yes Yes [-100, +100]
6 S. R. Griewank Yes Yes [-600, +600]
7 S. R. Rastrigin Yes Yes [-5, +5]
Execute
$ cd repo
$ ./demo <parameter setting> or make run (with default parameters)
Clean up
$ make clean
TODO

[1] J. Brest, V. Zumer and M. S. Maucec, “Self-Adaptive Differential Evolution Algorithm in Constrained Real-Parameter Optimization,” 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, 2006, pp. 215-222. doi: 10.1109/CEC.2006.1688311, URL

[2] CUDA is a parallel computing platform and programming model developed by NVIDIA for GPGPU