مدل ,partheno genetic algorithms چیست

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مدل ,partheno genetic algorithms چیست

مدل ,partheno genetic algorithms چیست

مدل ,partheno genetic algorithms چیست

مدل ,partheno genetic algorithms چیست

Genetic algorithm (GA) is a computational model that simulates the natural evolution of Darwin’s biological evolution theory and then find the optimal solution. Due to the crossover operator, the classical genetic algorithm cannot be used to solve the TSP directly if it generates illegal individuals violating the problem requirements. Some researchers have developed some special crossover operators which can be used to solve the traveling salesman problem through genetic algorithm. Besides that, improved genetic algorithms and partheno genetic algorithms (PGA) were developed for dealing with this problem. It is worthwhile noting that the same case can also happen in the MTSP.Each individual’s gene represents a feasible solution to the problem and each individual’s fitness means the fitness function value at the solution that its gene represents in the PGA. In the natural selection process, the individual with greater fitness is more likely to survive and produce offspring. Each feasible solution is represented by a kid’s gene caused by the perturbation of its parent’s gene. After the genes of next generation are identified, the evolution process continues. This algorithm is similar to the standard GA, except that PGA generates each kid by only one individual rather than two of them. In this way, crossover operator can be avoided in the process of producing the next generation. The pseudo code of PGA is given in Algorithm 1.As for an optimization problem where solution space has been defined clearly, PGA can be used conveniently to solve the problem. However, the reproduction process is a local search in the small area around the solutions that the current population represents since the kid’s gene comes from the genetic mutation of its parent. In other words, the algorithm depends much on the initial population and ignores other area in the solution space. As the amount of data increases, the solution space may also increase rapidly. In this case, the size of the searching area will not change much and the amount of solution ignored will increases greatly if the parameters of PGA are unchanged. As a result, the efficiency of the algorithm will become much lower

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