From the relevant discussions on function optimization covered in Chapters 3, 5, and 10, we by now should have realized that to find the global minimum or maximum of a multivariable function is in general a formidable task even though a search for an extreme of the same function under certain circumstances is achievable. This is the driving force behind the never-ending quest for newer and better schemes in the hope of finding a method that will ultimately lead to the discovery of the shortest path for a system to reach its overall optimal configuration.

The genetic algorithm is one of the schemes obtained from these vast efforts. The method mimics the evolution process in biology with inheritance and mutation from the parents built into the new generation as the key elements. Fitness is used as a test for maintaining a particular genetic makeup of a chromosome. The scheme was pioneered by Holland (1975) and enhanced and publicized by Goldberg (1989). Since then the scheme has been applied to many problems that involve different types of optimization processes (Bäck, Fogel, and Michalewicz, 2003). Because of its strength and potential applications in many optimization problems, we introduce the scheme and highlight some of its basic elements with a concrete example in this chapter. Several variations of the genetic algorithm have emerged in the last decade under the collective name of evolutionary algorithms and the scope of the applications has also been expanded into multi-objective optimization (Deb, 2001; Coello Coello, van Veldhuizen, and Lamont, 2002).