A parallel tabu search algorithm for large traveling salesman problems
Discrete Applied Mathematics
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A New Approach on the Traveling Salesman Problem by Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms and Neighbourhood Search
Selected Papers from AISB Workshop on Evolutionary Computing
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
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Genetic Algorithms (GAs) have been applied in many different fields and optimization problem domains. It is well known that it is hard to solve the complex problems with a Simple Genetic Algorithm (SGA). Many previous studies have shown that the hybrid of local search and GAs is an effective approach for finding near optimum solutions to the traveling salesman problem (TSP). In this paper, an approach based on the Genetic Recombination is proposed and applied to the TSP. The algorithm is composed of two SGAs which only consist of the basic genetic operators such as selection, crossover and mutation. One of the SGAs is named as the Global Genetic Algorithm (GGA) and carried out in the main tours which are designed for searching the global optimal solutions. Another one is named as the Local Genetic Algorithm (LGA) and carried out in the sub tours which are designed for searching the local optimal solutions. The LGA is combined to the GGA as an operator. The local optimal solutions are recombined to the main tours for improving the search quality. To investigate the features of the proposed algorithm, it was applied to a small double circles TSP and some interesting results were presented in our experiments.