Advances in genetic programming
Maté: a tiny virtual machine for sensor networks
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A survey of evolutionary and embryogenic approaches to autonomic networking
Computer Networks: The International Journal of Computer and Telecommunications Networking
Distributed genetic evolution in WSN
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Evolutionary computation techniques for intrusion detection in mobile ad hoc networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
P-CAGE: an environment for evolutionary computation in peer-to-peer systems
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Wireless Sensor Node Placement Using Hybrid Genetic Programming and Genetic Algorithms
International Journal of Intelligent Information Technologies
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Wireless sensor networks (WSNs) are medium scale manifestations of a paintable or amorphous computing paradigm. WSNs are becoming increasingly important as they attain greater deployment. New techniques for evolutionary computing (EC) are needed to address these new computing models. This paper describes a novel effort to develop a variation of traditional parallel evolutionary computing models to enable their use in the wireless sensor network. The ability to compute evolutionary algorithms within the WSN has innumerable advantages including intelligent-sensing, resource-optimized communication strategies, intelligent-routing protocol design, novelty detection, etc. In this paper we develop a parallel evolutionary algorithm suitable for use in a WSN. We then describe the adaptations required to develop practicable implementations to effectively operate in resource constrained environments such as WSNs. Several adaptations including a novel representation scheme, an approximate fitness computation method and a sufficient statistics based data reduction technique. These adaptations lead to the development of a GP implementation that is usable on the low-power, small footprint architectures typical to wireless sensor motes. We demonstrate the utility of our formulations and validate the proposed ideas using the algorithm to compute symbolic regression problems.