Abstract
In this paper, genetic algorithms for multi-objective optimization problems with uncertainty, which attract attention for applications to simulation-based and experiment-based optimization of real systems, are discussed. First, difficulties faced by conventional multi-objective GAs in their application to multi-objective optimization of noisy fitness functions are described. Second, to cope with these problems, a multi-objective GA that has a fitness estimation method and a new selection operator is proposed. The effectiveness of the proposed method is demonstrated by numerical simulations and real-world experiments.
