Evolutionary Computation 2.1 Conceptual Foundations of Evolutionary Computation

The goal of this chapter is to introduce the key algorithms and theory that constitute the core of Evolutionary computation. The principles and foundations of a basic genetic Algorithm are described in detail. In addition, a separate section discusses extensions to the simple Genetic Algorithm, such as reened crossover, mutation and selection schemes. and real coded representations. The chapter presents real-coded genetic Algorithms and messy genetic Algorithms which encode genetic information in a diierent way. The chapter concludes with an introduction to other types of Evolutionary Algorithms such as Evolution Strategies, Evolutionary programming and Genetic Programming. Evolutionary Algorithms (EAs) constitute a class of search and optimization methods, which imitate the principles of natural evolution (Goldberg, 1989; Holland, 1975). The common term Evolutionary Computation comprises techniques such as Genetic Algorithms, Evolution Strategies, Evolutionary Programming and Genetic Programming. Their principal mode of operation is based on the same generic concepts, a population of competing candidate solutions, random combination and alteration of potentially useful structures to generate new solutions and a selection mechanism to increase the proportion of better solutions. The diierent approaches are distinguished by the genetic structures that undergo adaptation and the genetic operators that generate new candidate solutions. Since Evolutionary Algorithms emulate natural evolution they adopt a 1