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An Adaptable Framework for Distributed Multi-Objective Search Algorithms Applied to the Genetic Programming of Sensor Networks

We present DGPF, a framework providing multi-objective, auto-adaptive search algorithms with a focus on Genetic Programming. We first introduce a Common Search API, suitable to explore arbitrary problem spaces with different search algorithms. Using our implementation of Genetic Algorithms as an example, we elaborate on the distribution utilities of the framework which enable local, Master/Slave, Peer-To-Peer, and P2P/MS hybrid distributed search execution. We also discuss how heterogeneous searches consisting of multiple, cooperative search algorithms can be constructed. Sensor networks are distributed systems of nodes with scarce resources. We demonstrate how Genetic Programming based on our framework can be applied to create algorithms for sensor nodes that use these resources very efficiently.