  Modern technological advancements constantly push forward the human-machine
interaction. Evolutionary Algorithms (EA) are an machine learning (ML) subclass
inspired by the process of natural selection - Survival of the Fittest, as
stated by the Darwinian Theory of Evolution. The most notable algorithm in that
class is the Genetic Algorithm (GA) - a powerful heuristic tool which enables
the generation of a high-quality solutions to optimization problems. In recent
decades the algorithm underwent remarkable improvement, which adapted it into a
wide range of engineering problems, by heuristically searching for the optimal
solution. Despite being well-defined, many engineering problems may suffer from
heavy analytical entanglement when approaching the derivation process, as
required in classic optimization methods. Therefore, the main motivation here,
is to work around that obstacle. In this piece of work, I would like to harness
the GA capabilities to examine optimality with respect to a unique combustion
problem, in a way that was never performed before. To be more precise, I would
like to utilize it to answer the question : What form of an initial droplet
size distribution (iDSD) will guarantee an optimal flame ? To answer this
question, I will first provide a general introduction to the GA method, then
develop the combustion model, and eventually merge both into an optimization
problem.
