Although ge ne tic algorithm has become very famous with its global searching, parallel computi ng, better robustness, and not needing differential information during evolution .However, it also has some demerits, suc...Although ge ne tic algorithm has become very famous with its global searching, parallel computi ng, better robustness, and not needing differential information during evolution .However, it also has some demerits, such as slow convergence speed. In this pap er, based on several general theorems, an improved genetic algorithm using varia nt chromosome length and probability of crossover and mutation is proposed, and its main idea is as follows:at the beginning of evolution, our solution with sho rter length chromosome and higher probability of crossover and mutation; and at the vicinity of global optimum, with longer length chromosome and lower probabil ity of crossover and mutation. Finally, testing with some critical functions sho ws that our solution can improve the convergence speed of genetic algorithm sign ificantly, its comprehensive performance is better than that of the genetic algo rithm which only reserves the best individual.展开更多
文摘Although ge ne tic algorithm has become very famous with its global searching, parallel computi ng, better robustness, and not needing differential information during evolution .However, it also has some demerits, such as slow convergence speed. In this pap er, based on several general theorems, an improved genetic algorithm using varia nt chromosome length and probability of crossover and mutation is proposed, and its main idea is as follows:at the beginning of evolution, our solution with sho rter length chromosome and higher probability of crossover and mutation; and at the vicinity of global optimum, with longer length chromosome and lower probabil ity of crossover and mutation. Finally, testing with some critical functions sho ws that our solution can improve the convergence speed of genetic algorithm sign ificantly, its comprehensive performance is better than that of the genetic algo rithm which only reserves the best individual.
