Schema representation in virus-evolutionary genetic algorithm for knapsack problem

This paper deals with a genetic algorithm based on virus theory of evolution (VE-GA). VE-GA simulates coevolution of a host population of candidate solutions and a virus population of substring representing schemata. In the coevolutionary process, the virus individuals propagate partial genetic information in the host population by virus infection operators. In this paper, we apply the proposed VE-GA to knapsack problems, and discuss the schema representation for solving the knapsack problem. Simulation results show that the virus infection explicitly uses effective schemata to search for optimal solutions, and the schema-based search can quickly increase the genotype frequency of effective schemata in the host population