Many approaches to AI in robotics use a multi-layered approach to
determine levels of behaviour from basic operations to goal-directed behaviour,
the most well-known of which is the subsumption architecture. In this paper, the
performances of the unigenic gene expression programming (ugGEP) and multigenic
GEP (mgGEP) in evolving robot controllers for a wall following robot is
analysed. Additionally, the paper introduces Regulatory Multigenic Gene Expression
Programming (RMGEP), a new evolutionary technique that can be utilised
to automatically evolve modularity in robot behaviour. The proposed technique
extends the mgGEP algorithm, by incorporating a regulatory gene as part of the
GEP chromosome. The regulatory gene, just as in systems biology, determines
which of the genes in the chromosome to express and therefore how the controller
solves the problem. In the initial experiments, the proposed algorithm is implemented
for a robot wall following problem and the results compared to that of
ugGEP and mgGEP. In addition to the wall following behaviour, a robot foraging
behaviour is implemented with the aim of investigating whether the position of
a speci c module (sub-expression tree (ET)) in the overall ET is of importance
when coding for a problem.http://link.springer.com/journal/107102016-05-30hb201
