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Genetic Algorithm is one of the most effective optimization algorithms, on which a lot of studies have been reported. Some studies on the application of island model, which is one of the representative methods to keep a diversity of solutions, to Multi-Objective Genetic Algorithm (MOGA) have been conducted. In MOGA, it is difficult to find the solutions which satisfy all objective functions because of their tradeoff. Especially when there are many objective functions, it is obvious that it needs a lot of time to search for effective Pareto solutions and find them. This paper proposes the interactive way of addition and deletion of islands to the original ones based on user's requirements with the visualization of acquired solutions in island model for MOGA. This paper applies the proposed method to Nurse Scheduling Problem (NSP) using the visualization by Principal Component Analysis (PCA). Through the experiment, it is confirmed that an interactive tuning of the weights for the objective functions leaded to the acquisition of better Pareto solutions which a user wants while they are difficult to be acquired by the prepared weights.
Evolutionary Computation (CEC), 2010 IEEE Congress on
Date of Conference: 18-23 July 2010