## 4 Experiments
### Number of Winning Games  
We calculated the winning rate by grouping 600 experimental data into five categories. Figure 6 shows the experimental results regarding the winning rate for four Records. Figure 6a shows that the winning rate is low, owing to the frequent forgetting problems, as only the CNN was used without relearning from the records. Figure 6b confirms that the winning rate gradually increases when the records were relearned. However, new moves of the GA AI continue to be created, which results in frequent variations in the winning rate. Figure 6c shows that the winning rate gradually increases, and all the wins were observed from the 525 th data, as seen in the green box area that is depicted in Figure 6c. The figure also shows that the result of learning all the possible moves that the GA AI can  
Figure 4: Screenshots of GuPyEngine. Figure 5: Proposed Gomoku system’s Python code in GupyEngine with Pycharm. perform to place the stone, as the ANN minimizes the forgetting problem. Gomoku is a game that requires the exact stone position to be determined. Therefore, the ideal result is derived when the distance \(\alpha\) of the ANN is five. Figure 6d-h show the results of the gradually increasing the \(\alpha\) of the ANN. The figures show that, as the \(\alpha\) of the ANN is higher than required, more errors occur, which hinders the learning. Figure 6d,e display similar shapes. ANN learning deteriorates the forgetting problem, which results in a decrease in the winning rate, as seen in the red box areas in Figure 6d,e. Figure 6h shows a lower winning rate than Figure 6a. Figure 7 shows the experimental results regarding the winning rate for three records. The results of Figure 7 show that, overall, the winning rate is lower than those seen in Figure 6. The proposed Gomoku system performs Enhanced CNN Training for each game. Therefore, the results of this experiment show that the winning rate increases nearing the end rather than the beginning. However, in Figure 7b-e, the red box area shows fewer learning records than in the previous experiment. In particular, Figure 7c confirms that the winning rate in the all-winning section, as shown in Figure 6c, also drops, owing to the lack of records. However, as in Figures 6 and 7c shows a very high winning rate when compared to other experimental cases presented in Figure 7, showing that the ANN \(\alpha\) of five corresponds to the optimized case in the proposed Gomoku system. Figure 6: Result of win rate when record count was four. Each graph \(y\)-axis refers to the number of wins from 0 to 5, and the \(x\)-axis refers to the number of games from 0 to 120. The figures show the experimental result using: (**a**) Only CNN without Record relearning; (**b**) CNN with Record relearning without ANN; (**c**) Proposed Gomoku system with ANN \(\alpha\):5; (**d**) Proposed Gomoku system with ANN \(\alpha\):10; (**e**) Proposed Gomoku system with ANN \(\alpha\):15; (**f**) Proposed Gomoku system with ANN \(\alpha\):20; (**g**) Proposed Gomoku system with ANN \(\alpha\):25; and, (**h**) Proposed Gomoku system with ANN \(\alpha\):30. Figures 8 and 9 show the experimental results regarding the winning rates for the two records and the single record, respectively. These results confirm that the number of records to be learned is insufficient and, therefore, the winning rate drops in all of the sections and the \"Self-Teaching\" occurs frequently. The experiments confirmed the satisfactory functioning of the ANN in the proposed Gomoku system when characterizing similar situations. We could also confirm that the optimal result was derived when the \(\alpha\) of the ANN was five. Furthermore, when the number of \(\alpha\) was greater than required, for example, in the range 25-30, it was observed that the CNN learning was rather hindered. Figure 7: Result of Win rate when Record count was three. Each graph \(y\)-axis refers to the number of wins from 0 to 5, and the \(x\)-axis refers to the number of games from 0 to 120.