Understanding the mechanism of GA is already hard, not to mention trying to interpret other people's code that implements it. That is even harder if you never code before.
Come back to the GA, you can create a model like this:
1. Represent each potential solution by a 40 gene-chromosome, where each gene represents a multiple choice answer.
2. The first question is represented by the first 4 genes, the second question the next 4 genes, and so on...
3. Permutation of the genes will generate different solutions for the population.
4. Put the population through the GA evolution, i.e. selection, crossover, mutation, and fitness evaluation. You should have already learnt it as part of your study. If not, read my articles on
Genetic Algorithms Demystified[
^] and
Genetic Algorithms Implementation[
^]
Last but not least, you should learn to code and implement it yourself. Only then will you really understand the working of GA.