Using a Simulated Annealing to Enhance Learning in Adjustment Processes
Albert Samà, Francisco Javier Ruiz, Núria Agell, Cecilio Ángulo · Frontiers in artificial intelligence and applications · 2009
This paper introduces a new approach to enhance learning in adjustment processes by using a support vector machine (SVM) algorithm as discriminant function jointly with an action generator module. The method trains a SVM with state-action patterns and uses trained SVM to select an appropriate action given a certain state in order to reach the target state. The system incorporates a simulated annealing technique to increase the exploration capacity and improve the ability to avoid local minima. The methodology has been tested in an example with artificial data.