Genetic algorithm to solve TSP problem

Genetic characteristics 1. Genetic Algorithm for Solving the set from the beginning Sao cable, rather than start from a single solution. This is a genetic algorithm optimization algorithm with the traditional great distinction. Traditional optimization algorithms are iterative initial value from a single optimal solution;Polycrystalline diamond into local optima. Genetic algorithms start the search from the string set, covering wide areas, which will help the global merit. 2. Genetic Algorithm is used very little information on a specific problem, easy to form generic Diamond blade. Fitness due to genetic algorithms use this information to search, and so does not need to issue derivative and problems directly related information. Fitness and genetic algorithm only general information string encoding, it can handle almost any problem. 3. Genetic algorithms have a strong fault tolerance The initial set of genetic algorithm itself with a lot of abrasive wheelsand the optimal solution far information; through selection, crossover and mutation operation can be quickly ruled out the optimal solution varies greatly with string; This is a strong filtering process; and is a parallel filtering mechanism. Therefore, genetic algorithm has a high fault tolerance. 4. Genetic algorithm selection, crossover and mutation operation are random, rather than determine the precise rules. This shows that the genetic algorithm approach is the use of stochastic optimal solution search, select the optimal solution to the looming reflects the cross reflects the optimal solution generated variation reflects the global optimum coverage.