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In this paper, a hybrid search algorithm with Hopfield neural network (HNN) and Genetic algorithm (GA) is proposed. The HNN method is first used to generate valid solutions which are considered as solutions for initial population of genetic algorithm. Then, GA is used to perform exploitation around the best solution at each evaluation. The proposed algorithm has both the advantages of HNN and GA that...
This paper proposes a new solution for Traveling Salesman Problem (TSP), using genetic algorithm. A heuristic crossover and mutation operation have been proposed to prevent premature convergence. Presented operations try not only to solve this challenge by means of a heuristic function but also considerably accelerate the speed of convergence by reducing excessively the number of generations. By considering...
This paper addresses an attempt to evolve genetic algorithm by a particular modified partially mapped crossover method to make it able to solve the Traveling Salesman Problem. Which is type of NP-hard combinatorial optimization problems. The main objective is to look a better GA such that solves TSP with shortest tour. First we solve the TSP by using PMX (Goldberg and Lingle, 1985) and then a modified...
In order efficiently to obtain an approximate solution of the traveling salesman problem (TSP), extended changing crossover operators (ECXOs) which can substitute any crossover operator of genetic algorithms (GAs) and ant colony optimization (ACO) for another crossover operator at any time is proposed. In our study ECXO uses both of EX (or ACO) and EXX (edge exchange crossover) in early generations...
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