Generating extrema approximation of analytically incomputable functions through usage of parallel computer aided genetic algorithms
This work addresses computational bottlenecks in optimization for researchers and engineers dealing with complex functions, though it appears incremental as it focuses on parallelization and operator modifications of existing genetic algorithms.
This paper tackles the problem of approximating extrema for analytically incomputable functions by using parallelized genetic algorithms, achieving significant speed increases on multithreaded processors like Sandy Bridge cores.
This paper presents capabilities of using genetic algorithms to find approximations of function extrema, which cannot be found using analytic ways. To enhance effectiveness of calculations, algorithm has been parallelized using OpenMP library. We gained much increase in speed on platforms using multithreaded processors with shared memory free access. During analysis we used different modifications of genetic operator, using them we obtained varied evolution process of potential solutions. Results allow to choose best methods among many applied in genetic algorithms and observation of acceleration on Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores.