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
Efficient algorithms for computing the L2-discrepancy
Mathematics of Computation
Computational investigations of low-discrepancy sequences
ACM Transactions on Mathematical Software (TOMS)
On the L2-discrepancy for anchored boxes
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
A note on E. Thiémard's algorithm to compute bounds for the star discrepancy
Journal of Complexity
Mathematical and Computer Modelling: An International Journal
Applied Numerical Mathematics
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Proceedings of the 2009 SPEC Benchmark Workshop on Computer Performance Evaluation and Benchmarking
Optimizing low-discrepancy sequences with an evolutionary algorithm
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Generalized Halton sequences in 2008: A comparative study
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Computational investigations of scrambled Faure sequences
Mathematics and Computers in Simulation
Ectropy of diversity measures for populations in Euclidean space
Information Sciences: an International Journal
Simple tools for multimodal optimization
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Evolutionary optimization of low-discrepancy sequences
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A rigorous runtime analysis for quasi-random restarts and decreasing stepsize
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Hi-index | 7.29 |
One of the best known low-discrepancy sequences, used by many practitioners, is the Halton sequence. Unfortunately, there seems to exist quite some correlation between the points from the higher dimensions. A possible solution to this problem is the so-called scrambling. In this paper, we give an overview of known scrambling methods, and we propose a new way of scrambling which gives good results compared to the others in terms of L"2-discrepancy. On top of that, our new scrambling method is very easy to implement.