Two Kinds Of Improved Genetic Algorithm To Solve The Optimal Solution Of The Optimization Problem

Genetic algorithm is used to solve optimization problems in computationalmathematics, it is a kind of evolutionary algorithm. In this paper, we proposetwo kinds of improved genetic algorithm for the defects of the traditional geneticalgorithm in solving the optimal solution of the optimization problem: genetic algo-rithm based on the strategy of small populations and a new evolutionary algorithmwith improved genetic operator and crossover strategy. The improved genetic algo-rithm can be more widely applied in solving the optimal solution of the optimizationproblem、computer science、mathematics and some practical problems.First, this paper propose a new genetic algorithm based on the strategy ofsmall populations for the premature phenomenon of the traditional genetic algo-rithm. We make some improvements on the standard genetic operators, and thenuse multiple small population parallel computing on the equal amount individuals,and use various methods to produce interaction between groups, in the process ofthe genetic algorithm calculation is simple and it’s efective to prevent the prema-ture phenomenon; Second,for the blindness of the genetic operator, we propose anadaptive genetic algorithm, which uses a new initialization method to make thepopulation is uniformly distributed within the solution space, and then adjust thecrossover probability and mutation probability to optimize crossover operator andmutation operator, this paper proposes a new crossover strategy at the same time,in order to make the algorithm more efective. After each of the new improvedalgorithms there are test functions and numerical examples.The main content of this paper is two kinds of improved genetic algorithm, andfnally the numerical example proved the results are efective.