Reactive Tabu Search and Sensor Selection in Active Structural Acoustic Control Problems
Journal of Heuristics
A Fast Heuristic Global Learning Algorithm for Multilayer Neural Networks
Neural Processing Letters
The Suitability of Particle Swarm Optimisation for Training Neural Hardware
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Evolutionary and adaptive synthesis methods
Formal engineering design synthesis
Efficient Learning in Adaptive Processing of Data Structures
Neural Processing Letters
Variable step search algorithm for feedforward networks
Neurocomputing
A hybrid algorithm for artificial neural network training
Engineering Applications of Artificial Intelligence
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In this paper the task of training subsymbolic systems is considered as a combinatorial optimization problem and solved with the heuristic scheme of the reactive tabu search (RTS). An iterative optimization process based on a “modified local search” component is complemented with a meta-strategy to realize a discrete dynamical system that discourages limit cycles and the confinement of the search trajectory in a limited portion of the search space. The possible cycles are discouraged by prohibiting (i.e., making tabu) the execution of moves that reverse the ones applied in the most recent part of the search. The prohibition period is adapted in an automated way. The confinement is avoided and a proper exploration is obtained by activating a diversification strategy when too many configurations are repeated excessively often. The RTS method is applicable to nondifferentiable functions, is robust with respect to the random initialization, and effective in continuing the search after local minima. Three tests of the technique on feedforward and feedback systems are presented