a ranking based adaptive evolutionary operator genetic algorithm(RAOGA) is proposed.RAOGA uses ranking operator to rank the individuals according to their fitness value
and the probabilities of select operator
crossover operator
mutation operator are adapted to ranking value of the individual
in the meantime
the probability of select is adapted to evolving process.Then
we prove its performance of convergence to the global optimum by means of Markov chain analysis.At last
the experimental result shows that the RAOGA converges to the global optimum more rapidly than that of the conventional genetic algorithms.