Genetic Algorithms For Several Kinds Of Optimization

Genetic algorithm is one kind of self-organized and adaptive random searching algorithm that simulates the evolution process and mechanism in nature for the optimization problems. Its coding technique and genetic operator are simpler. It has few requests to the condition of constraints in an optimization problem. It is very good at parallel and global search. It can apply to a wide range of the problems such as machine learning, pattern recognition, image disposal, optimization controller, combinatorial optimization, manage decision-making and so on.First, the origination and development, basic principle and basic framework of genetic algorithm are introduced in the thesis.Next, the bi-level programming problem (BLPP) is studied. Note that bi-level programming problem has wide applications, thus the research on algorithms for this problem is of great significance. Unfortunately, it is very difficult to determine its solution because of its inherent non-convexity and non-differentiability. In particular, it is more difficult to get a globe optimal solution of non-linear BLPP. For a special class of nonlinear bi-level programming, by using the monotonicity of the related functions, the feasible solution set of the lower-level problem is divided into a certain number of bounded intervals, and the bi-level programming can be transformed into several parallel and independent one-level programming problems. In this way, the original problem is simplified. For upper-level programming, a genetic algorithm is designed and its global convergence is proved. At last the numerical simulation results show that the algorithm is more effective and robust.Finally, in order to enhance the efficiency of the training algorithm in the back-propagation (BP) neural networks, a GA-PSO algorithm is proposed by integrating genetic algorithm (GA) and particle swarm optimization (PSO) technique. In GA-PSO, new individuals are created not only by crossover and mutation operations in GA, but also by PSO technique using redefined local optimization swarm. So it can both avoid local minimum and have good global search ability. By simulations on 3-odd-even-model and IRIS pattern-classification model, the superiority of GA-PSO to both GA and PSO in the weight training of the artificial neural networks is demonstrated.