Self-Organizing Genetic Algorithm: A Survey
Amouda Nizam, Buvaneswari Shanmugham · 2013
Self-organization systems are an increasingly attractive dynamic processes without a central control, emerge global order from local interactions in a bottom up approach. The advantage of blending the concept of self-organization enhances the working efficiency of other techniques to find a solution of huge search problem. Genetic Algorithms (GA) is such a technique, inspired by the natural evolution process, used to solve difficult optimization problem of large space solution, for an example, multiple sequence alignment (MSA) problem in a bioinformatics research. Self-organization technique automates the selection of appropriate parameter values of GA during execution without the user’s intervention. An attempt towards applying Self-organizing Genetic Algorithm (SOGA) on MSA requires a complete knowledge of the various parameters of SO and its relationships. This lead us to make a complete survey on inherent properties of SO and the method of blending GA in order to develop a self-organizing genetic algorithm (SOGA) for MSA. The aim of the research is to make use of the efficiency of GA without getting any input from the nontrained users to tune the parameters in order to achieve the expected result.