A genetic algorithm for determining nonadditive set functions in information fusion
As a classical aggregation tool, the weighted average method is widely used in information fusion. It is the Lebesgue integral with respect to the weights essentially. Due to some inherent interaction among diverse information sources, the weighted average method does not work well in many real problems. To describe the interaction, an intuitive and effective way is to replace the additive weights with a nonadditive set function defined on the power set of the set of all information sources. Instead of the weighted average method, we should use the Choquet integral or some other nonlinear integrals, especially, the new nonlinear integral introduced by the authors recently. The crux of making such an improvement is how to determine the nonadditive set function from given input-output data when the nonlinear integral is viewed as a multiinput single-output system. In this paper, we employ a specially designed genetic algorithm to realize the optimization in determining the nonadditive set function. (C) 1999 Elsevier Science B.V. All rights reserved.
genetic algorithms nonadditive set functions information fusion nonlinear integrals optimization