Improved Adaptive Genetic Algorithm for Solving Multi-Parameter Problems
An improved adaptive genetic algorithm is proposed to solve multi-parameter problems with low con-vergence accuracy,slow convergence speed and easy to fall into local optimization.The algorithm introduces a replica-tion operator,population density function and elite selection strategy,and proposes an adaptive strategy to adjust the crossover probability and mutation probability according to the population iteration times and individual fitness,which balances the global search ability and local optimization ability of the genetic algorithm.By solving the test function and traveling salesman problem,it is proved that the convergence accuracy and convergence speed of the improved a-daptive genetic algorithm are significantly improved.
Replication operatorAdaptive crossover operatorAdaptive mutation operatorPopulation density functionTest functionTravelling salesman problem
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