12 Genetic Algorithms
    This chapter is wholly dedicated to the amazing family of techniques known as genetic algorithms. As a matter of fact, not so amazing, since similar principles in nature have produced complex entities such as humans. This chapter starts by describing the basic properties and motivation underlying the genetic algorithm approach. After outlining the approach, the genetic operators are introduced and the whole approach is exemplified with respect to a simple example. The next section covers the important concept of schemata , introduced by Holland as part of his approach to develop a theoretical basis for genetic algorithms, and the schemata theorem. These concepts are discussed in terms of the bandit problem. The last section in this chapter discuss how to control the cost of calculating the fitness function for large populations in terms of sampling the individuals. The concept is illustrated for a simple situation. This section concludes by discussing the interesting issue of co-evolution of parasites, which explores the idea that the competition between two species tend to improve the fitness of both.