in these cases 30-50 pixels were set. The fitness function was at 
the beginning only the correctly detected road pixels, but very 
quickly it was recognized that the length/area weighting is 
indispensable. The initial population size was 100 genes, the 
number of generations was 100 to 200 thousand epochs. Within 
an epoch just one gene was modified by the genetic rules 
(mutation and crossover). Since the crossover seemed too 
drastic modifier in the rectangle keypoints’ coordinates, it was 
ignored in the later runs. 
The initial randomly generated population has been laid on the 
segmented image, as Fig. 5 demonstrates. 
Initial population 
  
Figure 5. Initial population on the segmented image 
During the computation the fitness of all genes are calculated, 
then a cumulated fitness are derived, which is excellent to 
describe the population. The average running time for training 
is between 1300 and 3600 s. 
The best candidates of the final generation (Fig. 6) can be 
linked by human operator in the later phase (so the unnecessary 
oblique rectangles can be deleted). 
The second test series was executed with the differential 
evolution. This procedure implements an algorithm, where three 
different genes must be selected, then the mutation modifies the 
first gene. Because of the drastic effect of crossover, it has been 
avoided also in this phase. The population size is roughly the 
same as with GA, but the runtime is definitely lower: 40-50 s. 
Even with 200 population size and 200 generations it requires 
—50 s CPU time. The reason is that this philosophy modifies all 
genes in a single run. The mutation factor was set for 1.0. 
The training can be monitored by the cumulative fitness 
function, which aggregates the scores for all genes within an 
epoch. Fig. 7 shows a typical decrease during 150 generations. 
  
Figure 6. Best 50 candidates of the final GA generation 
Population scores 
24: r 
  
23% 
2: 
21H 
20r 
15- eg 
  
50 100 A 
Figure 7. Population cumulative fitness during the evolution 
The differential evolution has a drawback, namely the 
continuously mutated genes can be modified in a way that they 
start resembling each other; in critical case the population 
converges into one single position. To avoid that negative 
feature, the training is monitored by the variances of the genes. 
If there are enough variations within the population, the 
variances of the coordinates are high. Resembling can be 
detected by dramatical decrease in the variance diagram. A 
healthy variance plot can be seen in Fig. 8. 
Population variability 
350r = - EE 
300 - 
250r 
2007 
150 i 
1008 
Bg 50 100 150 
Figure 8. Variance plot to keep track on resembling 
The differential evolution method is a global optimization 
technique, so the uniformization can be “dangerous”. If one has 
  
  
  
  
  
  
  
  
  
  
  
  
   
  
  
   
   
  
  
   
  
   
   
   
   
   
   
   
   
   
  
  
  
  
  
  
   
  
   
   
  
    
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