Genetic algorithms: A stochastic improvement technique. Tools, skills, pitfalls and examples

Abstract

In this chapter we try to show an introductory study addressed, mainly, to the postgraduate student. Because oí the extention and multiple branches of this stochastic technique, our proposal has been to provide a simplified panoramic view o~ the Genetic Algorithms field (GAs). / To begin with, we intent to mostrate sorne results, tools, and references in order to give an approximation to the "state of the art"; therefore we will start with a description of the main aspects and operators of GAs. Creativity and innovation are strong features in the field of GAs which are, sometimes, more an art than a science as they strongly depend on severa! 'fine tune' parameters which play a decissive role in the algorithm success. On the other hand we present severa! examples of the application of GAs in sorne simple test cases, including sorne new proposals, and finally we comment on our experiences with this "improvement" algorithm applied to severa! problems.