Eight Queens with Evolutionary Computing

3.3 Population
A population is a multi-set of genotypes (i.e. we should assume that there
may be individuals who are identical or very close in their fitness score). The
population forms the unit of evolution (i.e. individuals do not evolve, but the
population does) [1].
3.4 Parent selection
The role of parent selection is to select individuals based on their fitness score.
In nature too parents mate based on their fitness score. In biology this is often
broadly called sexual selection. Together with survival selection parent selection
is one of two forces that pushes quality.
3.5 Variation operators
There are two fundamental forces that form the basis of evolutionary systems:
• Variation operators
• Selection
Variation operators (recombination and mutation) create the necessary diversity
and thereby facilitate novelty to deal with environmental challenges.
Selection on the other hand acts as a force to push quality [1].
3.6 Purpose
In evolutionary computing algorithms perform stochastically, i.e. for a given set
of inputs number of iterations reaching a solution varies in each run.
There are also other limitations such as it is possible that the program may
climb a local hill rather than a global one, i.e. the program may not be able to
find the best set of solutions in a given run.
Nevertheless these known limitations are irrelevant for our purpose. The objective
of this study is not to maximise performance of evolutionary algorithms
(which may not necessarily perform better against deterministic algorithms) but
to demonstrate certain aspects of evolution, these are:
• Evolutionary process makes a given population increasingly better at being
adapted to the environment.
• Effects of mutation on diversity.
• Genetic drift in small populations.
4