I have implemented a genetic algorithm to find the evolutionary outcomes of a biological scenario. I simulate the evolution (i.e. optimization) of five traits in my model. I ran my code 100 times and it produced acceptable results, from biological stand-point. I want to highlight that, for biological reasons, my initial population was not very diverse. If I suppose my population size is m and my algorithm runs for n generations, an output of my algorithm is a m*n table. In a specific cell of this table, called table[i][j], I saved the values of five traits for the ith individual in the jth generation (i between 1 and m, j between 1 and n).
To discuss my data biologically, I want to know that if there is any specific pattern(s) in finding optimal solutions or not? In other words, is there an algorithm that I can use to find a hypothetical (consensus) "trajectory", that my individuals crossed to reach the optimal point(s)? For example, is there any specific pattern(s) in changes in genes (traits)? Or, is there any specific relationship between variables?
PS: To visualize my data, I calculate the average value for each gene (parameter) in each generation and plot the genes' values against generations. To give you an intuitive understanding of the results, here are five examples of these plots.