The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
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
Don't trust parallel Monte Carlo!
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
Journal of Global Optimization
How Random Generator Quality Impacts GA Performance
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
On Random Numbers And The Performance Of Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Contiguous binomial crossover in differential evolution
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
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Experiences in the field of Monte Carlo methods indicate that the quality of a random number generator is exceedingly significant for obtaining good results. This result has not been demonstrated in the field of evolutionary optimization, and many practitioners of the field assume that the choice of the generator is superfluous and fail to document this aspect of their algorithm. In this paper, we demonstrate empirically that the requirement of high quality generator does not hold in the case of Differential Evolution.