Efficiency of Search Performance through visualizing Search Process

Performance in searching solutions by Multi-Objective Genetic Algorithm (MOGA) depends on genetic operators and/or their parameters. For comparison of the performance with some genetic operators and/or parameters, it has been usually employed the transitions of fitness values through actual applications or the number/performance of acquired Pareto solutions in multi-optimization problems. This paper proposes a visualizing method of search process for MOGA, which can visualize relative distances among chromosomes in search process and give information of not only the performance but also the effects of the genetic operations such as the diversity of chromosomes. This method uses Self-Organizing Map (SOM) for the visualization. This paper applies Non Dominated Sorting Genetic Algorithm-II (NSGA-II) to FON test function and shows obtained nondominated solutions and visualization results. This paper also shows that the visualized data enables us to interpret the differences in search processes and to get new information to determine efficient genetic operators and their parameters. In addition, this paper applies the proposed method to Nurse Scheduling Problem (NSP), which is one of the complex multi-objective optimization problems in real world, and shows effective feedback to genetic operations can be done.