Bitwise operations for GPU implementation of genetic algorithms
Martín Pedemonte, Enrique Alba, Francisco Luna · 2011
Research on the implementation of evolutionary algorithms in graphics processing units (GPUs) has grown in recent years since it significantly reduces the execution time of the algorithm. A relevant aspect, which has received little attention in the literature, is the impact of the memory space occupied by the population in the performance of the algorithm, due to limited capacity of several memory spaces in the GPUs. In this paper we analyze the differences in performance of a binary Genetic Algorithm implemented on a GPU using a boolean data type or packing multiple bits into a non boolean data type. Our study considers the influence on the performance of single point and double point crossover for solving the classical One-Max problem. The results obtained show that packing bits for storing binary strings can reduce the execution time up to 50%.