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2020 | OriginalPaper | Chapter

Optimization of Generalized Halton Sequences by Differential Evolution

Authors : Pavel Krömer, Jan Platoš, Václav Snášel

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

Many practical applications such as multidimensional integration and quasi–Monte Carlo simulations rely on a uniform sampling of high–dimensional spaces. Halton sequences are d–dimensional quasirandom sequences that fill the d–dimensional hyperspace uniformly and can be generated with low computational costs. Generalized (scrambled) Halton sequences improve the properties of plain Halton sequences in higher dimensions by digit scrambling. Discrete nature–inspired optimization methods have been used to search for scrambling permutations of d–dimensional generalized Halton sequences that minimized the discrepancy of the generated point sets in the past. In this work, a continuous nature–inspired optimization method, the differential evolution, is used to optimize generalized Halton sequences.

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Title: Optimization of Generalized Halton Sequences by Differential Evolution
Authors: Pavel Krömer
Jan Platoš
Václav Snášel
Publisher: Springer International Publishing
Book: Learning and Intelligent Optimization
Print ISBN: 978-3-030-38628-3

Electronic ISBN: 978-3-030-38629-0

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-38629-0_30

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