  What really sparked my interest was how certain parameters worked better at
executing and optimization algorithm convergence even though the objective
formula had no significant differences. Thus the research question stated:
'Which parameters provides an upmost optimal convergence solution of an
Objective formula using the on-the-fly method?' This research was done in an
experimental concept in which five different algorithms were tested with
different objective functions to discover which parameter would result well for
the best convergence. To find the correct parameter a method called
'on-the-fly' was applied. I run the experiments with five different
optimization algorithms. One of the test runs showed that each parameter has an
increasing or decreasing convergence accuracy towards the subjective function
depending on which specific optimization algorithm you choose. Each parameter
has an increasing or decreasing convergence accuracy toward the subjective
function. One of the results in which evolutionary algorithm was applied with
only the recombination technique did well at finding the best optimization. As
well that some results have an increasing accuracy visualization by combing
mutation or several parameters in one test performance. In conclusion, each
algorithm has its own set of the parameter that converge differently. Also
depending on the target formula that is used. This confirms that the fly method
a suitable approach at finding the best parameter. This means manipulations and
observe the effects in process to find the right parameter works as long as the
learning cost rate decreases over time.
