Adaptive genetic algorithm based on distance measurement
一种基于距离测度的自适应遗传算法

In order to increase convergence probability and evolving speed of the adaptive genetic annealing algorithm based on distance measurement, an improved algorithm was proposed. The algorithm defined the mutation probability according to distance density and fitness, adopted improved crossover and simulated aimealing operation. In addition, when population tended to be uniform, it reserved the best individual and reproduced other individuals. Applying the improved realcoded genetic algorithm based on distance measurement to function optimization problem with boundary constraints, the simulative results show that the improved algorithm is more effective in realizing the high convergence probability and rapid evolving speed.