In order to derive a genetic algorithm, potential solutions to a problem need to be developed by encoding; for instance by using binary bit strings. Another method used is the estimation of distribution algorithms for software optimisation, whereby a probability model is maintained for each generation of a problem situation. While there are studies applying each of these software optimization methods separately, the number of studies where both methods are compared in the same assessment are not as plentiful.
This study seeks to carry out a comparison of both these software optimization methods in one particular study. Both these methods will be compared and an estimation of the efficacy of each method in terms of software optimization capacity will be made on the basis of the comparison. This study will rely primarily on two major research studies which are detailed below, which form a close parallel to the subject of this research study.
The most relevant studies that would apply in the context of this research proposal are those showing different methods of software optimization. Through the use of parallel and distributing processing, multi thread techniques have been shown to provide better solutions than sequential options (Cruz and Pelta, 2009). Under the multi thread option, each solver thread represents a particular optimization algorithm and a coordinator collects performance information on the solvers and then sends them instructions on how behaviour is to be altered. On the algorithmic approach, metaheuristics can be successfully applied to complex and difficult combinations of optimization programs. In the study carried out by Cruz and Pelta (2009), the most basic ingredients of soft computing were used, i.e, through fuzzy sets and fuzzy rules, depending upon the location, i.e, by focusing upon the p-median, where the combinatorial optimization problem occurred.