Organic Evolution and Problem Solving

Abstract

Evolutionary Algorithms (EAs), the topic of this work, is an interdisciplinary research field with a relationship to biology, Artificial Intelligence, numerical optimization, and decision support in almost any engineering discipline. Therefore, an attempt to cover at least some of these relations must necessarily result in several introductory pages, always having in mind that it hardly can be complete. This is the reason for a rather voluminous introduction to the fundamentals of Evolutionary Algorithms in section 1.1 without giving any practically useful description of the algorithms now. At the moment, it is sufficient to know that these algorithms are based on models of organic evolution, i.e., nature is the source of inspiration. They model the collective learning process within a population of individuals, each of which represents not only a search point in the space of potential solutions to a given problem, but also may be a temporal container of current knowledge about the “laws” of the environment. The starting population is initialized by an algorithm-dependent method, and evolves towards successively better regions of the search space by means of (more or less) randomized processes of recombination, mutation, and selection. The environment delivers a quality information (fitness value) for new search points, and the selection process favors those individuals of higher quality to reproduce more often than worse individuals. The recombination mechanism allows for mixing of parental information while passing it to their descendants, and mutation introduces innovation into the population. This process is currently used by three different mainstreams of Evolutionary Algorithms, i.e. Evolution Strategies (ESs), Genetic Algorithms (GAs), and Evolutionary Programming (EP), details of which are presented in chapter 2. This chapter presents their biological background in order to have the necessary understanding of the basic natural processes (section 1.1). Evolutionary Algorithms are then discussed with respect to their impact on Artificial Intelligence and, at the same time, their interpretation as a technique for machine learning (section 1.2). Furthermore, their interpretation as a global optimization technique and the basic mathematical terminology as well as some convergence results on random search algorithms as far as they are useful for Evolutionary Algorithms are presented in section 1.3.