M.Sc. Tezi Görüntüleme | |||||||||||||||||||||
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| Summary: One of the stochastic search methods, Genetic Algorithm, benefits three fundamental processes called Selection, Crossover and Mutation to get the best result in solution space. The goal of the algorithm is to constitute better descents with good charasteristics by crossing parents satisfying criteria. Individuals are frequently mutated to create variation, to gain good features, to reach to the best solution. The technique of Tabu Search forbids reinvestigation of these solutions for a while by keeping list of solution analyzed during the search. The searching process can reach to several nodes of the solution spaces owing to this attitude. The technique prevents non-releasing on the local optimum by choosing the best neighbor which is not prohibited (or even if prohibited, the best neighbor providing some criteria). In the work, Traveling Salesman Problem is approached with modified & developed Tabu Search and Genetic Algorithm, and the results of both algorithms are discussed with aspect of optimality. Keywords: Tabu Search, Genetic Algorithms, Intelligent Agents, Traveling Salesman Problem, Approximate Algorithms, Heuristic Algorithms, Optimization, Components | |||||||||||||||||||||