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
Computational investigations of low-discrepancy sequences
ACM Transactions on Mathematical Software (TOMS)
A generalized discrepancy and quadrature error bound
Mathematics of Computation
Faster evaluation of multidimensional integrals
Computers in Physics
When are quasi-Monte Carlo algorithms efficient for high dimensional integrals?
Journal of Complexity
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Co-evolving Parallel Random Number Generators
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Generating and Testing the Modified Halton Sequences
NMA '02 Revised Papers from the 5th International Conference on Numerical Methods and Applications
The effective dimension and quasi-Monte Carlo integration
Journal of Complexity
One more experiment on estimating high-dimensional integrals by quasi-Monte Carlo methods
Mathematics and Computers in Simulation - Special issue: 3rd IMACS seminar on Monte Carlo methods - MCM 2001
Variance Reduction via Lattice Rules
Management Science
Low discrepancy sequences in high dimensions: How well are their projections distributed?
Journal of Computational and Applied Mathematics
Generalized Halton sequences in 2008: A comparative study
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Good permutations for deterministic scrambled Halton sequences in terms of L2-discrepancy
Journal of Computational and Applied Mathematics
On the optimal Halton sequence
Mathematics and Computers in Simulation
Algorithms (x, sigma, eta): quasi-random mutations for evolution strategies
EA'05 Proceedings of the 7th international conference on Artificial Evolution
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Mathematical and Computer Modelling: An International Journal
Deterministic design for neural network learning: an approach based on discrepancy
IEEE Transactions on Neural Networks
Constructing low star discrepancy point sets with genetic algorithms
Proceedings of the 15th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Low-discrepancy sequences provide a way to generate quasi-random numbers of high dimensionality with a very high level of uniformity. The nearly orthogonal Latin hypercube and the generalized Halton sequence are two popular methods when it comes to generate low-discrepancy sequences. In this article, we propose to use evolutionary algorithms in order to find optimized solutions to the combinatorial problem of configuring generators of these sequences. Experimental results show that the optimized sequence generators behave at least as well as generators from the literature for the Halton sequence and significantly better for the nearly orthogonal Latin hypercube.