| 초록 |
Genetic algorithm based on the reproductive process of living organisms is being used to solve various optimization problems. It does not require special constraints and can be applied regardless of the type of problem. However, there are drawbacks that the criterion for terminating the algorithm depends on the user's arbitrary judgment, and the more complex the problem, the faster the calculation speed. Clustering can be used as a way to compensate for this. The K-means algorithm, the most commonly used clustering technique, has disadvantages: the number of clusters must be specified by the user and finding the global optimal is NP-hard problem. In order to solve this problem, we introduce a new function using distance and angle between solution vectors. Reinforced genetic algorithm can reduce the randomness of users and secure mathematical reliability by using statistical estimation. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (NRF-2016R1A5A1009592) |
