Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Self-Adaptive Genetic Algorithm for Numeric Functions
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Adapting operator settings in genetic algorithms
Evolutionary Computation
Performance of TCP over satellite networks under severe cross-traffic using GA
International Journal of Mobile Communications
Optimal switch location in mobile communication networks using hybrid genetic algorithms
Applied Soft Computing
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Although many studies have focused on testing computer networks under realistic traffic loads by means of genetic algorithms (GAs), little attention has been paid to optimising the parameters of the GAs in this problem. The objective of this work is to design and validate a system that, given some constraints on traffic bandwidth, generates the worst-case traffic for a given computer network and finds the traffic configuration (critical background traffic) that minimises throughput in that computer network. The proposed system is based on a meta-GA, which is combined with an adaptation strategy that finds the optimum values for the GA control parameters and adjusts them to improve the GA's performance. To validate the approach, different comparisons are performed with the goal of assessing the acceptable optimisation power of the proposed system. Moreover, a statistical analysis was conducted to ascertain whether differences between the proposed system and other algorithms are significant. The results demonstrate the effectiveness of the system and prove that, when the background traffic is driven by a GA that uses the parameters obtained from the system, the computer network's performance is much lower than when the traffic is generated by Poisson statistical processes or by other algorithms. This system has identified the worst traffic pattern for the protocol under analysis.