Towards a rational methodology for using evolutionary search algorithms
Evolutionary search algorithms (ESAs from now on) are iterative problem solvers developed with
inspiration from neo-Darwinian survival of the fittest genes. This thesis looks at the core issues
surrounding ESAs and is a step towards building a rational methodology for their effective use.
Currently there is no such method of best practice rather the whole process of designing and using
ESAs is seen as more of a black art than a tried and tested engineering tool. Consequently, many
non-practitioners are sceptical of the worth of ESAs as a useful tool at all.
Therefore the first task of the thesis is to layout the reasons, from computational theory, why
ESAs can be a potentially powerful tool. In this context the theory of NP-completeness is introduced
to ground the discussions throughout the thesis. Then a simple framework for describing
ESAs is developed to form another cornerstone of these later discussions. From here there are two
main themes of the thesis. The first theme is that the No Free Lunch result requires us to take a
problem centric, as opposed to algorithm centric, perspective on ESA research. The second major
theme is the argument that whole algorithms and traditional computer science problem classes are
the wrong level of granularity for the focus of our research. Instead we should be researching
empirical questions of search bias at the granularity of the components of search algorithms. Furthermore,
we should be finding empirical evidence to demonstrate that our granularity of analytic
class is such that one analytic class maps onto one search bias class.
We will see that this can mean that we have to sub-divide our classic computer science problems
classes into smaller sub-classes. The hope is that we can find analytic distinctions that will
sub-divide the instances along lines that match the divisions between the various empirically discoverable
search bias classes. The intention is to develop our knowledge until we get one analytic
class to map into one empirical class. If we have strong empirical evidence to suggest that this has
been achieved then we have good grounds on which to confidently use this knowledge to predict
the effective search biases required for new problem instances.
In the last two chapters of the thesis we demonstrated these ideas on various instances of the
euclidean TSP problem class