Based On Genetic Algorithm Optimization Problems

The combined portfolio optimization problem is the key foundation of modern engineering and many related fields. With the expansion of the scale of the problem, the search space of portfolio optimization problem will dramatically expand. For such complex problem, the main equation is its satisfactory answer. Genetic algorithm is a new type random search optimization method that simulates the biological evolution. In the field of portfolio optimization, Genetic algorithm is researched and applied widely, and has good performance in resolving many typical portfolio optimization problem.While in the application of science management and economic decision, there are lots of objective optimization problems in the realistic world. For the TSP, people should consider the multi-objective in practice, such as the shortest way, the least time, the slowest cost ,the smallest risk and so on. How to seek a fair and proper method is a complicated problem. This essay mainly researches for using genetic algorithm for multi-objective problem.This thesis'main research is the use of genetic algorithm to solve the classic TSP, through constructing appropriate genetic algorithm framework and establishing an effective genetic operation. In the design of algorithm, the thesis discusses the use of the classical genetic algorithm and the improved genetic algorithm to solve the TSP, and through computer simulation analysis of the results to certificate the correctness of the algorithm, as well as through the adjustment of related parameters to discuss the main factors affecting algorithm, finally certificates the genetic algorithm is superiority in optimization problems.