Genetic Algorithms (GAs) have been widely applied to Multi-objective Optimization Problems (MOPs), called MOGA. A set of Pareto solutions in MOPs having plural fitness functions are searched, then GA is applied in a multipoint search. MOGA performance decreases with the increasing number of objective functions because solution space spreads exponentially. An effective MOGA search is an important issue in Manyobjective Optimization Problems (MaOPs), which are MOPs with four or five objective functions or more. One effective approach is aggregation of objective functions and reducing their number, but appropriate aggregation and the number of objective functions to be aggregated has not been studied sufficiently. Our purpose here is to determine the effects of aggregation of objective functions quantitatively. This paper studies the effects of aggregation with the number of aggregated objective functions based on the evaluation criteria proposed in this paper when MOGA is applied to a Nurse Scheduling Problem (NSP).
