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    STUDIA INFORMATICA - Issue no. 2 / 2004  
         
  Article:   HEURISTICS AND LEARNING APPROACHES FOR SOLVING THE TRAVELING SALESMAN PROBLEM.

Authors:  GABRIELA ŞERBAN, CAMELIA-MIHAELA PINTEA.
 
       
         
  Abstract:  In present, all known algorithms for NP-complete problems are requiring time that is exponential in the problem size. Heuristics are a way to improve time for determining an exact or approximate solution for NP- complete problems. In this article is introduced and solved a problem based on a generaliza- tion of the Traveling Salesman Problem. We compare two classical algorithm results for the application: Branch and Bound and Nearest Neighbor and also two Ant Algorithms: Ant System and Ant Colony System. Being sto- chastic algorithms, Ant Algorithms have the solutions chosen according to a probability, which depends on the pheromone level, therefore they can be also considered as reinforcement learning techniques. We also propose a reinforcement Q-learning method for solving the Trav- eling Salesman Problem.  
         
     
         
         
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