Human-guided simple search: combining information visualization and heuristic search
Proceedings of the 1999 workshop on new paradigms in information visualization and manipulation in conjunction with the eighth ACM internation conference on Information and knowledge management
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Visualization of Pareto-Sets in Evolutionary Multi-Objective Optimization
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
IEEE Transactions on Visualization and Computer Graphics
Reactive and dynamic local search for max-clique: Engineering effective building blocks
Computers and Operations Research
Dynamic local search for the maximum clique problem
Journal of Artificial Intelligence Research
Designing and tuning SLS through animation and graphics: an extended walk-through
SLS'07 Proceedings of the 2007 international conference on Engineering stochastic local search algorithms: designing, implementing and analyzing effective heuristics
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Because of their high dimensionality, combinatorial optimization problems are often difficult to analyze, and the researcher's intuition is insufficient to grasp the relevant features. In this paper we present and discuss a set of techniques for the visualization of search landscapes aimed at supporting the researcher's intuition on the behavior of a Stochastic Local Search algorithm applied to a combinatorial optimization problem. We discuss scalability issues posed by the size of the problems and by the number of potential solutions, and propose approximate techniques to overcome them. Examples generated with an application (available for academic use) are presented to highlight the advantages of the proposed approach.