Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language
Exploring Grammatical Evolution for Horse Gait Optimisation
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
A genetic programming approach to automated software repair
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Self-Optimization of femtocell coverage to minimize the increase in core network mobility signalling
Bell Labs Technical Journal - Core and Wireless Networks
Foundations in Grammatical Evolution for Dynamic Environments
Foundations in Grammatical Evolution for Dynamic Environments
Analysis of a digit concatenation approach to constant creation
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
On the limiting distribution of program sizes in tree-based genetic programming
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Grammar-based Genetic Programming: a survey
Genetic Programming and Evolvable Machines
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Self-optimized coverage coordination in femtocell networks
IEEE Transactions on Wireless Communications
A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Evolving behaviour trees for the Mario AI competition using grammatical evolution
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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
IEEE Communications Magazine
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We study grammars used in grammatical genetic programming (GP) which create algorithms that control the base station pilot power in a femtocell network. The overall goal of evolving algorithms for femtocells is to create a continuous online evolution of the femtocell pilot power control algorithm in order to optimize their coverage. We compare the performance of different grammars and analyse the femtocell simulation model using the grammatical genetic programming method called grammatical evolution. The grammars consist of conditional statements or mathematical functions as are used in symbolic regression applications of GP, as well as a hybrid containing both kinds of statements. To benchmark and gain further information about our femtocell network simulation model we also perform random sampling and limited enumeration of femtocell pilot power settings. The symbolic regression based grammars require the most configuration of the evolutionary algorithm and more fitness evaluations, whereas the conditional statement grammar requires more domain knowledge to set the parameters. The content of the resulting femtocell algorithms shows that the evolutionary computation (EC) methods are exploiting the assumptions in the model. The ability of EC to exploit bias in both the fitness function and the underlying model is vital for identifying the current system and improves the model and the EC method. Finally, the results show that the best fitness and engineering performances for the grammars are similar over both test and training scenarios. In addition, the evolved solutions' performance is superior to those designed by humans.