Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
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 grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Journal of Global Optimization
On Random Numbers And The Performance Of Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Biostatistical Analysis (5th Edition)
Biostatistical Analysis (5th Edition)
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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This article presents an empirical study of the impact of the change of the Random Number Generator over the performance of four Evolutionary Algorithms: Particle Swarm Optimisation, Differential Evolution, Genetic Algorithm and Firefly Algorithm. Random Number Generators are a key piece in the production of e-science, including optimisation problems by Evolutionary Algorithms. However, Random Number Generator ought to be carefully selected taking into account the quality of the generator. In order to analyse the impact over the performance of an evolutionary algorithm due to the change of Random Number Generator, a huge production of simulated data is necessary as well as the use of statistical techniques to extract relevant information from large data set. To support this production, a grid computing infrastructure has been employed. In this study, the most frequently employed high-quality Random Number Generators and Evolutionary Algorithms are coupled in order to cover the widest portfolio of cases. As consequence of this study, an evaluation about the impact of the use of different Random Number Generators over the final performance of the Evolutionary Algorithm is stated.