Abstract
AbstractThe design of efficient monitoring networks is critical for a better understanding of environmental, ecological, and epidemiological processes. In this paper, we develop for the optimal design of monitoring networks a new hybrid genetic algorithm (HGA) which combines the standard genetic algorithm (GA) with a local search (LS) operator. We compare the performance of our HGA with two other stochastic search algorithms, a simulated annealing (SA) algorithm and a standard GA. Specifically, we consider the reduction of pre‐existing large‐scale monitoring networks, when the optimality criterion is the maximization of the entropy of the included stations. The algorithms were tested on a set of simulated datasets of different sizes, as well as on a real application involving the downsize of a large‐scale environmental monitoring network. In each of the cases considered the HGA outperformed the other two algorithms. Copyright © 2009 John Wiley & Sons, Ltd.
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