Evolutionary algorithms are based on randomness, i.e. each run can have a slightly different outcome. Therefore, it is not possible to compare the results of only one run. Instead, we need to repeat the run a number of times and compare the performance in the average/best/worst case.
The submitted solution must contain the following:
To create the plot with the fitness you can use the scripts createGraphs.ps1 (for Windows PowerShell), or createGraphs.sh(for Unix). Both scripts require to have gnuplot installed and in $PATH.
The scripts expect as an input the log of the performance of the algorithm. Each line in the file contains six numbers - the number of fitness evaluation up to this point, and 5 numbers describing the statistical properties of the fitness of the best individual in each of a number of runs - minimum, first quartile, average, third quartile, and maximum. Such files are produced by the available source codes, or you can produce them yourself (if you prefer programming in other languages and want to use the scripts to make the graphs).
Using the scripts is quite easy. They require only two parameters, which indicate where are their inputs (the outputs of the evolutionary algorithm) a how they should be named in the plot. Assume, we have outputs of two algorithms in files logs/basic.objective_stats and logs/better.objective_stats. To compare these algorithms, we have to run
createGraphs.ps1 -logFileNames basic,better -legendNames Basic,Better
This yields the comparison of the two algorithms in one plot named output.svg, the names in the legend will read “Basic” and “Better”.
This script has more useful parameters:
These parameters can be used to make the resulting plot more readable. Use them and experiment with them. Try to make the plots as readable as possible.
A Python script is also available to create the graphs. It does not need gnuplot, but uses numpy and matplotlib instead. The parameters of the script are similar to those of the scripts described above. Check them in the source code.
The following hints may help you to make better graphs