|The main objective in automatic robot controller development is to devise mechanisms
whereby robot controllers can be developed with less reliance on human developers. One
such mechanism is the use of evolutionary algorithms (EAs) to automatically develop
robot controllers and occasionally, robot morphology. This area of research is referred
to as evolutionary robotics (ER). Through the use of evolutionary techniques such as
genetic algorithms (GAs) and genetic programming (GP), ER has shown to be a promising
approach through which robust robot controllers can be developed.
The standard ER techniques use monolithic evolution to evolve robot behaviour: monolithic
evolution involves the use of one chromosome to code for an entire target behaviour.
In complex problems, monolithic evolution has been shown to suffer from bootstrap problems;
that is, a lack of improvement in fitness due to randomness in the solution set
[103, 105, 100, 90]. Thus, approaches to dividing the tasks, such that the main behaviours
emerge from the interaction of these simple tasks with the robot environment
have been devised. These techniques include the subsumption architecture in behaviour
based robotics, incremental learning and more recently the layered learning approach
[55, 103, 56, 105, 136, 95]. These new techniques enable ER to develop complex controllers
for autonomous robot. Work presented in this thesis extends the field of evolutionary robotics by introducing Gene
Expression Programming (GEP) to the ER field. GEP is a newly developed evolutionary
algorithm akin to GA and GP, which has shown great promise in optimisation problems.
The presented research shows through experimentation that the unique formulation of
GEP genes is sufficient for robot controller representation and development. The obtained
results show that GEP is a plausible technique for ER problems. Additionally, it is shown
that controllers evolved using GEP algorithm are able to adapt when introduced to new
Further, the capabilities of GEP chromosomes to code for more than one gene have been
utilised to show that GEP can be used to evolve manually sub-divided robot behaviours.
Additionally, this thesis extends the GEP algorithm by proposing two new evolutionary
techniques named multigenic GEP with Linker Evolution (mgGEP-LE) and multigenic
GEP with a Regulator Gene (mgGEP-RG). The results obtained from the proposed algorithms
show that the new techniques can be used to automatically evolve modularity
in robot behaviour. This ability to automate the process of behaviour sub-division and
optimisation in a modular chromosome is unique to the GEP formulations discussed, and
is an important advance in the development of machines that are able to evolve stratified
behavioural architectures with little human intervention.