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Abstract
In this thesis a new Virus Transmission Genetic Algorithm is presented based on the modern progress of biological evolution. The VTGA simulates the evolution of immune defense and the infection transmission model. Simulating biological infections, this algorithm contains one virus population and one host population as well as several new operations, including virus infection, virus spread and virus evolution. To study the effectiveness of the VTGA, we apply it to several function optimization problems, several travelling salesman problems and two forest planning problems. We discuss in this thesis how the VTGAs performance reacts to different configurations of parameters. Results of experiments show that the VTGA performs well at searching optimal solutions and preserving diversity of population.