Evolutionary transition on Virus-Evolutionary Genetic Algorithm

The paper deals with a genetic algorithm (GA) based on the virus theory of evolution (VEGA) and evolutionary transition of a population. VEGA can self adaptively change the searching ratio between local search and global search according to the current state of population of candidate solutions. In addition, various types of evolutionary optimization methods have been proposed and successfully applied to many optimization problems. However, it is difficult to determine the coding method, genetic operators and selection scheme. To analyze the behavior of GAs, Markov chain analysis, deceptive problems and schema analysis have been discussed. We discuss evolutionary transition concerning fitness improvement through numerical simulation of the traveling salesman problem. The simulation results indicate that particular genetic operators give a population different potentialities for generating candidate solutions and that virus infection operators can generate effective schemata and propagate them to a population evolved with any genetic operators.