Good random number generators are (not so) easy to find
Selected papers from the 2nd IMACS symposium on Mathematical modelling---2nd MATHMOD
Hierarchical parallel coordinates for exploration of large datasets
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
VizCraft: a problem-solving environment for aircraft configuration design
Computing in Science and Engineering
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Visual hierarchical dimension reduction for exploration of high dimensional datasets
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Using Curves to Enhance Parallel Coordinate Visualisations
IV '03 Proceedings of the Seventh International Conference on Information Visualization
Exploring N-dimensional databases
VIS '90 Proceedings of the 1st conference on Visualization '90
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
Information Visualization: Perception for Design
Information Visualization: Perception for Design
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Tracer spectrum: a visualisation method for distributed evolutionary computation
Genetic Programming and Evolvable Machines
Information Sciences: an International Journal
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In this article we apply information visualization techniques to the domain of swarm intelligence. We describe an intuitive approach that enables researchers and designers of stochastic optimization algorithms to efficiently determine trends and identify optimal regions in an algorithm's parameter search space. The parameter space is evenly sampled using low-discrepancy sequences, and visualized using parallel coordinates. Various techniques are applied to iteratively highlight areas that influence the optimization algorithm's performance on a particular problem. By analyzing experimental data with this technique, we were able to gain important insight into the complexity of the target problem domain. For example, we were able to confirm some underlying theoretical assumptions of an important class of population-based stochastic algorithms. Most importantly, the technique improves the efficiency of finding good parameter settings by orders of magnitude.