abstract = "This dissertation aims on analysing fundamental
concepts and dogmas of a graph-based genetic
programming approach called Cartesian Genetic
Programming (CGP) and introduces advanced genetic
operators for CGP. The results of the experiments
presented in this thesis lead to more knowledge about
the algorithmic use of CGP and its underlying working
mechanisms. CGP has been mostly used with a
parametrization pattern, which has been prematurely
generalized as the most efficient pattern for standard
CGP and its variants. Several parametrization patterns
are evaluated with more detailed and comprehensive
experiments by using meta-optimization. This thesis
also presents a first runtime analysis of CGP. The time
complexity of a simple (1+1)-CGP algorithm is analysed
with a simple mathematical problem and a simple Boolean
function problem. In the subfield of genetic operators
for CGP, new recombination and mutation techniques that
work on a phenotypic level are presented. The
effectiveness of these operators is demonstrated on a
widespread set of popular benchmark problems.
Especially the role of recombination can be seen as a
big open question in the field of CGP, since the lack
of an effective recombination operator limits CGP to
mutation-only use. Phenotypic exploration analysis is
used to analyze the effects caused by the presented
operators. This type of analysis also leads to new
insights into the search behaviour of CGP in continuous
and discrete fitness spaces. Overall, the outcome of
this thesis leads to a reconsideration of how CGP is
effectively used and extends its adaption from Darwin's
and Lamarck's theories of biological evolution.",