A comparative study of Pseudo and Quasi random sequences for the solution intergral equations
Journal of Computational Physics
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Quasi-random sequences and their discrepancies
SIAM Journal on Scientific Computing
Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
A generalized discrepancy and quadrature error bound
Mathematics of Computation
When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?
Journal of Complexity
Parallel and Distributed Computing Issues in Pricing Financial Derivatives through Quasi Monte Carlo
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Genetic algorithms using low-discrepancy sequences
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
DCMA: yet another derandomization in covariance-matrix-adaptation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Good permutations for deterministic scrambled Halton sequences in terms of L2-discrepancy
Journal of Computational and Applied Mathematics
Log(λ) modifications for optimal parallelism
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Simple tools for multimodal optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A rigorous runtime analysis for quasi-random restarts and decreasing stepsize
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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[10,22] presented various ways for introducing quasi-random numbers or derandomization in evolution strategies, with in some cases some spectacular claims on the fact that the proposed technique was always and for all criteria better than standard mutations. We here focus on the quasi-random trick and see to which extent this technique is efficient, by an in-depth analysis including convergence rates, local minima, plateaus, non-asymptotic behavior and noise. We conclude to the very stable, efficient and straightforward applicability of quasi-random numbers in continuous evolutionary algorithms.