Editing Code Guide (section)

==GA Module==
The GA module contains all the functions required to run the GA
as we have for this project. The functions are:

generate_parent: Generates a list of specified length containing
randomly generated values between 0 and 1. This is
a real valued parent that can be used in a fitness function
to determine a fitness as required. It is a simple matter to
instead make discrete parents if that is what is required.

generate_population: Generates a population of parents using
the generate_parent function. This population can be
any integer value.

mutate: This function traverses a parent and mutates genes based
on a mutation probability. A mutation involves replacing
the gene with a randomly generated new gene. In this case,
the genes are real valued in the range [0, 1], but could be
modified to discrete as required.

crossover: The crossover function takes a population as its main
input and outputs a new population of the same size that
has undergone the crossover procedure. Crossover requires
pairs of parents and each parent is only considered once
per generation and so the population size must be divisible
by two to ensure all parents are considered. An error will
be raised if the population is not divisible by two.

tournament_selection: This function takes a population then
pairs the parents up at random and compares the fitness
of each pair. The parent with the better fitness is kept
while the other parent is discarded. This function outputs
a population that is half the size of the input population
and so should generally be run twice in each generation to
ensure population size is consistent throughout the simulation.
As the function pairs up parents, the population size
must again be divisible by two.

fitness: This is the key user determined function for the GA.
This function will change depending on the aims, constraints
and requirements of each simulation. The function must
utilise a parent to determine its output or else the GA will
essentially be a fancy random number generator.

main: This function puts all the other functions together to allow
simple operation of the GA. The steps of the main function
are described generally in figure 4.