Mitchell, T. J. (2010) An exploration of evolutionary computation ...

Chapter 3
Background - Multimodal Optimisation
Since the introduction of EC over 40 years ago, there has been growing interest in the
application of EAs to an ever-increasing range of parameter optimisation problems. EAs
have been shown to be robust, reliable and straightforward to apply even when there is
very little a priori knowledge of the underlying problem domain. However, in search space
environments containing multiple distinct optima, EAs can often fail. This chapter reviews
an EA pathology known as preconvergence and summarises the algorithmic attributes that
result in this shortcoming. A range of techniques are then reviewed which have been
designed to minimise the likelihood of preconvergence.
3.1 Multimodal Problem
Domains and Preconvergence
As described in chapter two, EAs operate through the maintenance of a finite population of
solution candidates. Each candidate represents a sample taken from the fitness landscape of
the application domain. At the early stages of optimisation, samples are distributed
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