A Methodology for Solving Problems: Problem Modeling and Heuristic Generation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital neural networks
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Wireless and Mobile Network Architectures
Wireless and Mobile Network Architectures
Evolving Cellular Automata for Location Management in Mobile Computing Networks
IEEE Transactions on Parallel and Distributed Systems
A Comparison of Three Artificial Life Techniques for Reporting Cell Planning in Mobile Computing
IEEE Transactions on Parallel and Distributed Systems
Location Management in Mobile Computing
AICCSA '01 Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications
The Use of a Hopfield Neural Network in Solving the Mobility Management Problem
ICPS '04 Proceedings of the The IEEE/ACS International Conference on Pervasive Services
A Simulated Annealing Approach for Mobile Location Management
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
A Genetic Algorithm for Finding Optimal Location Area Configurations for Mobility Management
LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
A dynamic location management scheme for next-generation multitier PCS systems
IEEE Transactions on Wireless Communications
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This work presents a new approach to solve the location management problem by using the location areas approach. A combination of a genetic algorithm and the Hopfield neural network is used to find the optimal configuration of location areas in a mobile network. Toward this end, the location areas configuration of the network is modeled so that the general condition of all the chromosomes of each population improves rapidly by the help of a Hopfield neural network. The Hopfield neural network is incorporated into the genetic algorithm optimization process, to expedite its convergence, since the generic genetic algorithm is not fast enough. Simulation results are very promising and they lead to network configurations that are unexpected but very efficient.