Randomization tests
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Telecommunications Optimization: Heuristic and Adaptive Computation Techniques
Computational Intelligence in Telecommunications Networks
Computational Intelligence in Telecommunications Networks
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Wireless Resource Management Using Genetic Algorithm for Mobiles Equilibrium
ISCC '01 Proceedings of the Sixth IEEE Symposium on Computers and Communications
Parallel Genetic Algorithms for Third Generation Mobile Network Planning
PARELEC '04 Proceedings of the international conference on Parallel Computing in Electrical Engineering
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Automatic mesh generation for mobile network dimensioning using evolutionary approach
IEEE Transactions on Evolutionary Computation
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CDMA and WCDMA mobile phone networks depend on a network of antennae, each defining a geographic ‘cell' that handles the transmissions to and from users' handsets within that cell. These antennae have adjustable settings whose values have a large effect on both quality of service (and consequent subscriptions) and resource consumption. We consider the optimisation of these parameters, and describe experiments that compare a range of optimisation algorithms with the methods currently used in the field for this purpose. The aim of the current project was to achieve faster (necessary) and better (if possible) results than the existing methods used by field engineers. We find that certain evolutionary algorithm configurations achieve both of these requirements on test problems arising from real data from a high-traffic urban environment. To some extent the ideal algorithm depends on the size and load in the network being optimised, and this is the main topic of ongoing research.