| dc.description.abstract | Random Boolean Networks are a generalisation of binary Cellular Automata,
without a fixed topology. This thesis presents an RBN implementation using
an instruction-based approach, and compares this to a traditional table-based
approach. The implementations are used to evolve RBNs with maximum
attractor lengths, in order to investigate the evolvability and the usefulness
of an instruction-based implementation. The results show limited usefulness
for K = 2, but the instruction-based implementation performs significantly
better for K = 3. The instruction-based implementation is slower than the
table-based implementation by a factor of ∼ 10, but areas of improvement
have been identified and discussed. | |
