# Design of Warship Simulation Using Variable-Chromosome Genetic Algorithm
## 5 Discussion  
Figure 6a shows the results of the simulations, with 50 populations and 10 generations in which the fitness value was varied in order to confirm mission complexity differences. In the case with low mission complexity, tactics evolved that focused on evasion and reaching the final goal. These tactics stemmed from the priority being given to surviving. As a result, the average mission success rate converged to almost 0.17 after the fourth generation, of both the GA using the fixed chromosome and the GA using the variable chromosome. This similarity occurred because convergence with low tactics complexity is easy without reference to the number of chromosomes.  
However, in missions with a complexity value of at least 0.5, the mission success rate of the fixed-chromosome GA converged to 0.17 at the third generation, however, the mission success rate of the variable-chromosome GA converged to almost 0.34. In addition, when comparing how the tactics evolved, it was observed that the fixed-chromosome GA simply moved toward the goal while the variable-chromosome GA developed more complex offensive tactics, such as detecting, classifying, and attacking the enemy. However, the final mission success rate was only 0.34, because the difference between the physical specifications of the submarine and the surface ships was too large.  
These results occurred because with the fixed-chromosome GA, the limit on the number of rules meant that adaption could not take place for complex tactics, such as aggressive maneuvers. Yet, with the variable-chromosome GA, adaption took place for complex tactics. This shows that the variable-chromosome GA was able to evolve to create more outstanding tactics.  
Figure 6b is a graph comparing the number of chromosomes in accordance with the mission complexity. It represents the average of the top 10 of the final evolutions of each individual mission complexity. When the mission complexity was low, it was not necessary to evolve complex tactics, and so the number of chromosomes hardly increased. However, when the mission complexity was high, the number of chromosomes increased to almost 14 to adapt to the complex situation.  
Figure 7 shows the evolution among a different number of dummies in multiple simulations. As shown in the graph, the fitness value seems to be increasing as each generation evolves, regardless of the number of dummies in a simulation. However, it has a tendency to slow down its increasing rate of fitness value with more dummies, whereupon after the 4 th or 5 th generation, it shows little difference in curves. Figure 8 shows the changes in the number of chromosomes in every generation, in correlation to the number of dummies. The curve shows a relatively steep gradient in simulations with less than two dummies, yet, it appears converged if it involves more than three dummies. In an analysis of the final state, that no longer has any meaningful changes in the number of chromosomes, the research shows that it has evolved into evading the battles, while persisting toward the objective. It chooses rather simpler tactics without increasing the number of chromosomes in complex situations. It is speculated that the limitations have been induced due to the insufficient sets of rules in the simulations.  
Figure 6: Simulation results. (**a**) Mission success rate, (**b**) The number of chromosomes.