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A Genetic Local Search Algorithm for Solving Symmetric and Asymmetric Traveling Salesman Problems
 In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation
, 1996
"... The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to find local optima in a given TSP search space, and ..."
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Cited by 77 (12 self)
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The combination of local search heuristics and genetic algorithms is a promising approach for finding nearoptimum solutions to the traveling salesman problem (TSP). In this paper, an approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in order to find the global optimum. New genetic operators for realizing the proposed approach are described, and the quality and efficiency of the solutions obtained for a set of symmetric and asymmetric TSP instances are discussed. The results indicate that it is possible to arrive at high quality solutions in reasonable time. I. Introduction In the Traveling Salesman Problem (TSP) [18], [27], a number of cities with distances between them is given and the task is to find the minimumlength closed tour that visits each city once and returns to its starting point. A symmetric TSP (STSP) is one where the distance between any...
Interactive Genetic Algorithms for the Traveling Salesman Problem
 In Genetic and Evolutionary Computation Conf
, 1999
"... We use an interactive genetic algorithm to divide and conquer large traveling salesperson problems. Current genetic algorithm approaches are computationally intensive and may not produce acceptable tours within the time available. Instead of applying a genetic algorithm to the entire problem, we let ..."
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Cited by 3 (0 self)
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We use an interactive genetic algorithm to divide and conquer large traveling salesperson problems. Current genetic algorithm approaches are computationally intensive and may not produce acceptable tours within the time available. Instead of applying a genetic algorithm to the entire problem, we let the user interactively decompose a problem into subproblems, let the genetic algorithm separately solve these subproblems and then interactively connect subproblem solutions to get a global tour for the original problem. Our approach significantly reduces the computing time to find high quality solutions for large traveling salesperson problems. We believe that an interactive approach can be extended to other visually decomposable problems. 1 INTRODUCTION The traveling salesperson problem (TSP) is a classical example of an NPHard combinatorial optimization problem (Garey and Johnson, 1979). Given N cities and distances among them, the aim is to find the shortest tour that visits each cit...
International Journal of Management Science and Engineering Management
, 2007
"... Abstract. A Memetic Algorithm (MA) encompasses a class of approaches with proven practical success in variety of optimization problems aiming to make use of benefits of each individual approach. In this paper, we present a special designed Memetic Algorithm to solve the wellknown Symmetric Travelin ..."
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Abstract. A Memetic Algorithm (MA) encompasses a class of approaches with proven practical success in variety of optimization problems aiming to make use of benefits of each individual approach. In this paper, we present a special designed Memetic Algorithm to solve the wellknown Symmetric Traveling Salesman Problem (STSP).The main feature of the Memetic Algorithm is to use a local search combined with a special designed genetic algorithm to focus on the population of local optima. To check the performance quality of the proposed Memetic Algorithm, some benchmark problems are solved. Experiments on the benchmark set indicate that the Memetic Algorithm is quite efficient and competitive and produces good convergence behaviour and solutions.