Pavel Kromer
ABSTRACT
Complex problems are often addressed by methods from the domain of computational intelligence, including metaheuristic algorithms. Different metaheuristics have different abilities to solve specific types of problems and the selection of suitable methods has a large impact on the ability to find good problem solutions. Problem characterization became an important step in the application of intelligent methods to practical problems. A popular approach to problem characterization is the exploratory landscape analysis. It consists of a sequence of operations that approximate and describe the hypersurfaces formed by characteristic problem properties from a limited sample of solutions. Exploratory landscape analysis uses a particular strategy to select just a small subset of problem solutions for which are the characteristic properties evaluated and high-level landscape features computed. Low-discrepancy sequences have been recently used to design a family of sampling strategies. They have useful space-filling properties but their effective and efficient randomization might represent an issue. In this work, we study the Cranley-Patterson rotation, a lightweight randomization strategy for low-discrepancy sequences, compare it with other randomization methods, and observe the effect its use has on the randomization of sets of sampling points in the context of exploratory landscape analysis.
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Index Terms
- Randomization of Low-discrepancy Sampling Designs by Cranley-Patterson Rotation
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