Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Many hard examples for resolution
Journal of the ACM (JACM)
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Local Search Characteristics of Incomplete SAT Procedures
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
When gravity fails: local search topology
Journal of Artificial Intelligence Research
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A study of ACO capabilities for solving the maximum clique problem
Journal of Heuristics
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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In this paper, we propose a simple and efficient method for measuring the spatial dispersion of a set of points in a metric space. This method allows the quantifying of the population diversity in genetic algorithms. It can also be used to measure the spatial dispersion of any local search process during a specified time interval. We then use this method to study the way Walksat explores its search space, showing that the search for a solution often includes several stages of intensification and diversification.