Evolutionary Optimization Schemes And Applications Based On The Similitude Of Individuals

Many problems can be classified into optimization problems, whether from science test or from engineering design. But in reasonable computational time, traditional algorithms can not obtain approximate satisfied optima for large scale problem. However, evolutionary algorithms is competent for these problems, and is used widely to work out the with large scale optimization problems in practice.But traditional evolutionary scheme, which has the semi-blindness when producing offspring, can't achieve the balance between the efficiency and result. To solve the problem, we did the research as following:The dissertation consists of two parts: in the first part we study on the theory of evolutionary computation: A survey is made on the general theory of evolutionary computation. We proposed a new adaptive mutation operator based on the similitude between individuals. Based on the systematical survey on the One-Parent Genetic Algorithm, a new adaptive evolutionary algorithm using this operator is proposed. In the second part, we talk about the application of the new evolutionary algorithm to solving one-dimensional cutting stock problems. The experimental results show the advantageous performance of the algorithm. The main research work is listed as follows:In the first chapter, we give a brief introduction to the research content of evolutionary computation .Then we point out the key problem that must be solved in our research work.In the second chapter, we discuss the general theory of evolutionary computation, including its basic concepts, basic characteristics and fundamental procedure that one has to follow when designing evolutionary algorithms.The third chapter presents our research on real-code adaptive mutation operator based on the similitude between individuals. Based on the systematical survey on evolutionary optimization and One-Parent Genetic Algorithm, we propose a One-Parent mutation operator based on the similitude between real-code individuals. First of all, we defined the distance, similitude and neighborhoods of individual.Distance can reflect the difference between individuals, and similitude is designed to reflect how close two individuals are, and neighborhoods are used to realize the division of population. We proposed a new adaptive evolutionary algorithm based on similitude which has several characteristics. Firstly, it is capable for mutation operator to acquire "insight jumps" of the fitness; secondly, it can avoid the "semi-blind" of conventional Darwinian-type evolutionary computation. Thirdly, it ensures the stable converge of the algorithm into global optimum.The fourth chapter presents our application research on using the new evolutionary algorithm to solve one-dimensional cutting stock problems. The experimental results show the advantageous performance of the algorithmAt last, the research work in the dissertation is summarized, we suggests that more research work in this area should be done in the future and predicts bright prospect of future research.