Varying the Probability of Mutation in the Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Towards an Optimal Mutation Probability for Genetic Algorithms
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Adaptive design optimization of wireless sensor networks using genetic algorithms
Computer Networks: The International Journal of Computer and Telecommunications Networking
Analysing qos trade-offs in wireless sensor networks
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
MONSOON: A Coevolutionary Multiobjective Adaptation Framework for Dynamic Wireless Sensor Networks
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Simulating wireless and mobile networks in OMNeT++ the MiXiM vision
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
Analysis of the latency-lifetime tradeoff in wireless sensor networks
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Fast simulation methods to predict wireless sensor network performance
Proceedings of the 6th ACM symposium on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks
Knowledge-based design space exploration of wireless sensor networks
Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
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Wireless sensor networks (WSNs) consist of numerous sensor nodes with several possible configurations for each node. As there are a lot of nodes in a typical WSN, each with its own set of configurations, the number of configurations for the network as a whole is huge and the design space is extremely large. The configuration of a WSN has a strong effect on the quality of services of running applications and the performance of the WSN. Multi-objective evolutionary algorithms (EAs) are well suited to explore the trade-offs in a WSN design space. However, an EA has many configuration parameters in itself. This paper presents several guidelines for configuring a multi-objective EA for design space exploration, given a specification of the WSN to be configured and a time budget available for analysis. We demonstrate the effectiveness of these guidelines on a specific type of WSN that uses a gossip strategy for disseminating data over the network.