Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Foundations in Grammatical Evolution for Dynamic Environments
Foundations in Grammatical Evolution for Dynamic Environments
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
Dynamic environments can speed up evolution with genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
An analysis of the behaviour of mutation in grammatical evolution
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Automated optimization of service coverage and base station antenna configuration in UMTS networks
IEEE Wireless Communications
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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We analyse the impact of dynamic training scenarios when evolving algorithms for femtocells, which are low power, low-cost, user-deployed cellular base stations. Performance is benchmarked against an alternative stationary training strategy where all scenarios are presented to each individual in the evolving population during each fitness evaluation. In the dynamic setup, different training scenarios are gradually exposed to the population over successive generations. The results show that the solutions evolved using the stationary training scenarios have the best out-of-sample performance. Moreover, the use of a grammar which produces discrete changes to the pilot power generate better solutions on the training and out-of-sample scenarios.