Evolutionary Methods

Abstract

In this chapter we will refer quite often to the book of M. Mitchell. The reader will consult this biology inspired material for further details. The idea of mimicking Evolution as an optimisation tool for engineering problems appeared in the late 50s and early 60s. The concept was to evolve a population of candidate solutions to solve a problem, using operators inspired by natural selection. In the 60s, Rechenberg introduced “Evolution Strategies” (ESs) for airfoil design: this approach being continued by H. Schweffel. Other computer scientists developed evolution inspired algorithms for optimisation and machine learning at the same period when electronic computers appeared for the first time. Genetic Algorithms (GAs) were invented by J. Holland in the late 60s and developed by Holland and his students (D. Goldberg among many others) at the University of Michigan to study within the computer the phenomenon of adaptation as it occurs in nature. Holland’s bookon “Adaptation in Natural and Artificial Systems” presented the genetic algorithm as an abstraction of biological Evolution, which was a major innovation due to the biological concept of population, selection, crossover and mutation. The theoretical foundation of Genetic Algorithms (GAs) was built on the notion of “schemas” and “building blocks” which is explained in detail in many books devoted to Genetic Algorithms (cf. D. Goldberg for instance. In the last decade there has been widespread interaction among researchers studying Evolutionary Computation methods, and the GAs, Evolution Strategies, Evolutionary Programming and other evolutionary approaches were finally unified in the late 90s under the umbrella named Evolutionary Algorithms (EAs).