It has some new features,such as introducing a hybrid adaptive mutation operator based on Gaussian mutation,Cauchy mutation and Lévy mutation,using multi-parent search strategy and stochastic ranking strategy.

It is characterized with the following new features:1)introducing a hybrid self-adaptive crossover-mutation operator,which can enhance the search ability and exploit the optimum offspring;2)using a new constraint-handling technique to maintain the diversity of the population;3)simplifying the scaling factor F of the Original Differential Evolution(ODE)algorithm,which can red.

Application of genetic algorithm with adaptive mutation to Flow Shop Scheduling Problem;

Meanwhile,the adaptive mutation and annealing operation were used to adjust and optimize the population.

In order to ensure convergence and quicken converging speed, a policy of adaptive mutation and simulated annealing is adopted.

Second,by the self-adaptive mutation operator introduced to the algorithm,the information ex- changed between different sub-populations and the ability of PSO algorithm broke awa.

The evolutionary strategy of self-adaptive mutation step is used to search solutions in the globle area and local area.

Based on self-adaptive mutation of evolutionary strategy,it introduces the mutation rate which makes the algorithm pay its attention to global searching in the initial stage and local-tuning in the later stage,quickly get convergence to the true Pareto set.