Many practical problems we encounter in our daily life can be solved mathematically.For example,We can use mathematical modeling to calculate how to properly arrange the order of tasks in daily life and how to know the solution of the maximum,minimum or other extreme-value problems.The way of mathematical modeling is to change the problems in real life into the optimization problem of a numerical function,while genetic algorithm is the most commonly used and classic application in the problem of function optimization.Except genetic algorithms,the mathematical modeling methods of other algorithms often require a variety of objective functions,and the requirements are still very strict,usually requiring them to be inductive,differentiable and so on.Genetic algorithms,however,do not require these properties because genetic algorithms are computational models of biological evolution that mimic natural and genetic mechanisms and are a general approach to finding the optimal solution.The genetic laws usually follow selection,crossover and mutation operations on the chromosomes until the evolution are terminated.The appropriate function is used to evaluate the fitness value of individuals in the population.The algorithm uses a parallel global search method.And it does not depend on the specific types of problems and the specific circumstances,can solve a variety of complex system optimization problems.Although GA can solve all kinds of complex problems without depending on the problem itself,and can also effectively grasp the overall evolutionary direction,the relatively fixed control parameters in the optimization search of complex functions make the algorithm easily fall into the local extreme,be immature convergence,low search efficiency,and unstable.This study presents an improved adaptive genetic algorithm.Specifically,the crossover probability and the mutation probability were dynamically adjusted according to the concentrating and dispersing degree of the fitness values of the whole populations.In complex function optimization problems,the result of the simulation shows that the improved adaptive genetic algorithm has a great improvement in many aspects of the global optimization,such as the convergence rate,the optimal solution and the stability. |