Theory

Abstract

In this chapter we present a brief overview of some of the approaches taken to analysing and modelling the behaviour of Evolutionary Algorithms. The “Holy Grail” of these efforts is the formulation of predictive models describing the behaviour of an EA on arbitrary problems, and permitting the specification of the most efficient form of optimiser for any given problem. However, (at least in the authors’ opinions) this is unlikely ever to be realised, and most researchers will currently happily settle for techniques that provide any verifiable insights into EA behaviour, even on simple test problems. The reason for what might seem like limited ambition lies in one simple fact: evolutionary algorithms are hugely complex systems, involving many random factors. Moreover, while the field of EAs is fairly young, it is worth noting that the field of population genetics and evolutionary theory has a head start of more than a hundred years, and is still battling against the barrier of complexity.