Adaptation in natural and artificial systems
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Creating artificial life: self organization
Creating artificial life: self organization
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Parallel Computing - Special issue on cellular automata: from modeling to applications
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Principles of Mobile Communication
Principles of Mobile Communication
Genetic Algorithms in Search, Optimization and Machine Learning
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Cellular Radio and Personal Communications: Selected Readings
Cellular Radio and Personal Communications: Selected Readings
Industrial Applications of Genetic Algorithms
Industrial Applications of Genetic Algorithms
Cell Planning for Wireless Communications
Cell Planning for Wireless Communications
Cellular Automata and Cooperative Systems
Cellular Automata and Cooperative Systems
Exploiting the Selfish Gene Algorithm for Evolving Cellular Automata
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Cellular Automata
Resource sharing and coevolution in evolving cellular automata
IEEE Transactions on Evolutionary Computation
Locating strategies for personal communication networks, a novel tracking strategy
IEEE Journal on Selected Areas in Communications
Simulating Large Wireless Sensor Networks Using Cellular Automata
ANSS '05 Proceedings of the 38th annual Symposium on Simulation
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 Comprehensive Overview of the Applications of Artificial Life
Artificial Life
IEEE Transactions on Parallel and Distributed Systems
Clustering techniques for dynamic mobility management
Proceedings of the 4th ACM international workshop on Mobility management and wireless access
A Simulated Annealing approach for mobile location management
Computer Communications
Mobile computing: Opportunities for optimization research
Computer Communications
Clustering techniques for dynamic location management in mobile computing
Journal of Parallel and Distributed Computing
International Journal of Mobile Network Design and Innovation
Applying scatter search to the location areas problem
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Differential evolution for solving the mobile location management
Applied Soft Computing
A combined genetic-neural algorithm for mobility management
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Advances in Engineering Software
Evolutionary algorithms for location area management
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Self-deployment, self-configuration: critical future paradigms for wireless access networks
WAC'04 Proceedings of the First international IFIP conference on Autonomic Communication
Hyperspectral image segmentation through evolved cellular automata
Pattern Recognition Letters
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Location management is a very important and complex problem in mobile computing. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of location management scenarios. This paper investigates the use of cellular automata (CA) combined with genetic algorithms to create an evolving parallel reporting cells planning algorithm. In the reporting cell location management scheme, some cells in the network are designated as reporting cells; mobile terminals update their positions (location update) upon entering one of these reporting cells. To create such an evolving CA system, cells in the network are mapped to cellular units of the CA and neighborhoods for the CA is selected. GA is then used to discover efficient CA transition rules. The effectiveness of the GA and of the discovered CA rules is shown for a number of test problems.