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Genetic Algorithms are adaptive methods which may be used to solve search and optimization problems. Three basic operations in Genetic Algorithms are selection, crossover and mutation, an important problem using Genetic Algorithms is the premature convergence in local optimum. This paper presents an adaptive genetic algorithm which adjusts probability of mutation dynamically based on average square...
The standard genetic algorithm often meets the occurrences of slow convergence for solving combinatorial optimization. In this study, we present a hybrid genetic algorithm (LSHGA) for symmetric traveling salesman problem. In the algorithm, a modified local search method based on new neighborhood model was contrived as crossover operation. And a MUT3 operator was introduced as mutation operation. For...
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