A Hyperheuristic Approach to Scheduling a Sales Summit
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Towards Adaptive, Resilient and Self-organizing Peer-to-Peer Systems
Revised Papers from the NETWORKING 2002 Workshops on Web Engineering and Peer-to-Peer Computing
Ant Colony Optimization
How to Solve It: Modern Heuristics
How to Solve It: Modern Heuristics
A comprehensive analysis of hyper-heuristics
Intelligent Data Analysis
Collaboration Between Hyperheuristics to Solve Strip-Packing Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Stable solving of CVRPs using hyperheuristics
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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The computational optimization field defines the parameter tuning problem as the correct selection of the parameter values in order to stabilize the behavior of the algorithms. This paper deals the parameters tuning in dynamic and large-scale conditions for an algorithm that solves the Semantic Query Routing Problem (SQRP) in peer-to-peer networks. In order to solve SQRP, the HH_AdaNAS algorithm is proposed, which is an ant colony algorithm that deals synchronously with two processes. The first process consists in generating a SQRP solution. The second one, on the other hand, has the goal to adjust the Time To Live parameter of each ant, through a hyperheuristic. HH_AdaNAS performs adaptive control through the hyperheuristic considering SQRP local conditions. The experimental results show that HH_AdaNAS, incorporating the techniques of parameters tuning with hyperheuristics, increases its performance by 2.42% compared with the algorithms to solve SQRP found in literature.