Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
The SPMD Model: Past, Present and Future
Proceedings of the 8th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
Representations for Genetic and Evolutionary Algorithms
Representations for Genetic and Evolutionary Algorithms
Evolutionary Computing on Consumer Graphics Hardware
IEEE Intelligent Systems
Rapid evaluation and evolution of neural models using graphics card hardware
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
The Art of Computer Programming, Volume 4, Fascicle 1: Bitwise Tricks & Techniques; Binary Decision Diagrams
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Graphics processing units and genetic programming: an overview
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on advances in computational intelligence and bioinformatics
A many threaded CUDA interpreter for genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
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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%.