Shape optimization using an adaptive crossover operator genetic algorithms

Crossover operation is carried out using constant crossover probability and random interchange point in the standard genetic algorithm. This operation mode is blindfold and stochastic. It is not expected that the fitness value of sub-generation is larger than that of the parents. So an adaptive crossover operator is proposed The location of crossover and cross probability is adjusted according to fitness function, so that cross operation is performed along with convergence direction. The improved genetic algorithm is applied to compute a two-dimensional multi-modal function and study shape optimization of a centrifugal impeller in order to verify algorithmic rationality and validity. It shows that convergence performance is greatly enhanced.