The Traveling Tournament Problem Description and Benchmarks
CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
A simulated annealing approach to the traveling tournament problem
Journal of Scheduling
Improving Variable Selection Process in Stochastic Local Search for Propositional Satisfiability
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Managing Diversity on an AIS That Solves 3-Colouring Problems
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
Population-based simulated annealing for traveling tournaments
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Hybrid randomised neighbourhoods improve stochastic local search for DNA code design
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Inc*: an incremental approach for improving local search heuristics
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Adaptation of a multiagent evolutionary algorithm to NK landscapes
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper, we introduce a method which goal is to help the search done by a Stochastic Local Search algorithm. Given a set of initial configurations, our algorithm dynamically discriminates the ones that seems to give more promising solutions, discarding at the same time those which did not help. The concept of diversity is managed in our framework in order to both avoid stagnation and to explore the search space. To evaluate our method, we use a well-known local search algorithm. This algorithm has been specially designed for solving instances of the challenging Traveling Tournament Problem. We compare the performance obtained running different configurations of the local search algorithm to the ones using our framework. Our results are very encouraging in terms of both the quality of the solutions and the execution time required.