Abstract:
This thesis works with a new evolutionary system for feedforward artificial neural
networks (ANNs). An indirect encoding scheme, to be particular, modified cellular
encoding (MCE) is proposed to represent ANNs. The original cellular encoding is
modified in such a way that it does not suffer from the well-known permutation problem
or competing conventions problem of genetic algorithms for evolving ANNs. The
functionality of some program symbols in cellular encoding is changed; new rules are
added. As a consequence, it is possible to apply crossover operator in the genetic search.
Radical change of architecture i.e. behaviour from parents to their children is stopped by
keeping the application of crossover on genotypes within certain levels. It is shown in
this work that addition / deletion of nodes / conncctions can evidently be done by
crossover alone. Other attempts are also taken to minimize behavioural disruption
between parents and their offspring. In the evolution system, the number of user specified
parameters is also decreased.
The evolutionary system is also implemented and its performance is tested on some real
world problems. The upshot of the genetic search is studied and assessed against the
contemporary researches, although direct comparison with other evolutionary approaches
to designing ANN is very difficult. It is shown in this thesis that the genetic search can
find a reasonable ANN from the search space in considerably short period.
