Genetic Algorithm (GA) Methodology and Its Internal Working

Abstract

Many practitioners are shy with implementing GAs. Due to this, a lot of researchers avoid using GAs as problem-solving techniques. It is desirable that an implementer of GA must be familiar in working with high-level computer languages. Implementation of GA involves complex coding and intricate computations which are of a repetitive nature. GAs if not implemented with caution will result in vague or bad solutions. This chapter overcomes the obstacles by implementing and defining various data structures required for implementing a simple GA. They will write various functions of GA code in C ++ programming language. In this chapter, initial string population generation, selection, crossover, and mutation operator used to optimize a simple function (one variable function) coded as unsigned binary integer is implemented using C ++ programming language. Mapping of fitness issue is also discussed in application of GAs.