Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
On an Optimization Problem in Sensor Selection
Discrete Event Dynamic Systems
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Convex Optimization
Utility based sensor selection
Proceedings of the 5th international conference on Information processing in sensor networks
The sensor selection problem for bounded uncertainty sensing models
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Sensors: Theory, Algorithms, and Applications
Sensors: Theory, Algorithms, and Applications
On the convergence of a class of estimation of distribution algorithms
IEEE Transactions on Evolutionary Computation
Automatica (Journal of IFAC)
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In this paper, we apply Estimation-of-Distribution Algorithms (EDAs) to the problem of selecting a set of k sensors from m sensors for the purpose of parameter estimation. Unlike other evolutionary algorithms, in EDAs a new population of individuals in each iteration is generated without crossover and mutation operators; instead, a new population is generated based on a probability distribution, which is estimated form the best selected individuals of previous iteration. Our results indicate that EDA is a good candidate for solving the sensor selection problems.