Peter A. Whigham
Lecturer, Department of Information Science

Evolution as Search

Darwin's concept of natural selection gave a convincing explanation for the condition
of our natural environment. Elements from the theory of evolution may also be used as
a basis for constructing machine-based systems for induction and optimisation.

This seminar will introduce the concept of evolution as search.  A minimal set of
the abstract components required to create a population-based search will initially
be described.  Using this framework the machine learning techniques of Genetic Algorithms,
Genetic Programming, Evolving Neural Networks and Evolving Inductive Logic Programs will
be introduced.   Concepts such as fitness selection, mutation, crossover, genotype,
phenotype, convergence, fitness landscapes, coevolution and epistasis will be explained.

So are they actually useful?  Come along and find out what the answer is (and it's not
necessarily a resounding yes!).  A number of applications will be described which
demonstrate that they may have a role to play in model building and system process
understanding, especially when those systems are complex or highly non-linear.

This seminar is relevant to all researches interested in developing theories for
time-series, spatial or qualitative data.