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

Schutz (1996) and Bäck et al (2000).
The selection operator may be distinguished from the mutation and recombination
operators as it is entirely independent of the search space structure. As such, any selection
operator from one evolutionary algorithm may be easily applied to any other. As was
shown earlier, the GA traditionally employs a fitness proportionate probabilistic selection
operator. However, tournament selection (Goldberg and Deb, 1991) as well as linear
ranking selection (Baker, 1985) methods are also widely employed. On the other hand, the
ES regularly adopts a deterministic scheme. However, selection operators have also been
shared between these two classes: A truncation selection operator has been designed and
implemented for use within the Breeder GA (Mtihlenbein and Schlierkamp-Voosen, 1993),
which is based upon the deterministic techniques employed by human breeders.
Furthermore, deterministic selection-based GA developments have also been developed by
Affenzeller et al (2005) and Eshelman (1990). The tournament selection scheme employed
by Goldberg and Deb (1991) for use within the GA has also been adopted by the ES, as
described in Schwefel and Rudolph (1995). The ES plus selection strategy is also modelled
by the elitist selection or generation gap scheme in GAs (De Jong, 1975).
It is clear that the ideas and concepts that once separated the various implementations of
the EA are now shared between them. In his book Beyer even goes so far as to state that
the algorithms are only separated by the lack of theory that unites them (2002, p3). Recent
evolutionary computation publications are frequently concerned with hybrid or haptic
algorithms with ideas gleaned from the optimisation literature without bias. The relative
merits or detriments of one class of EA compared with another is a discussion which will
not appear here. The EAs proposed throughout chapters four and five are applied within
the framework of the ES; generalisation could easily be made to the GA but such
developments are beyond the scope of this thesis. For a side by side comparison of the
three main classes of EA (GA, ES and EP) see Bäck and Schwefel (1993).
2.5 Summary of this Chapter
In this chapter the computational model of evolution was reviewed, with details of how the
model may be applied to optimise static real-valued problems. The specifics of the GA and
ES were introduced with a brief summary of their historic developments and current state.
Detailed implementation specifics were provided for ES, as this forms the theoretical
framework within which the algorithmic developments documented in chapters four and
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