Comparison of Standard and Micro-GA

Having chosen the evolution parameters that provided the best results for both the standard and the micro-GA (Tables 1 and 2), I will now compare the performance of the two in solving the current problem.

The top panels of Figure  show a comparison of convergence rates between the standard genetic algorithm (left) and the micro-genetic algorithm (right). The difference in convergence rate is in the first generations is impressive. For example, the micro-genetic algorithm would have essentially converged after 2000 cost-function evaluations, whereas the standard genetic algorithm would take almost 10000 cost-function evaluations to reach the same convergence level. If enough iterations (generations) are allowed both algorithms will converge to essentially the same result. The bottom panels in Figure  show the corresponding trace match. The differences are not too great because both algorithms were essentially run to convergence.

 

final_comparison
Figure 11 Comparison between the standard and the micro genetic algorithm. Top panels convergence rates for standard genetic algorithm (left) and micro genetic algorithm (right). Bottom panels show the corresponding trace match with continuous line representing the original trace and the dotted line the inverted ones.