An investigation into Cellular Automata: The Self-Modifying Instruction-Based Approach

In this thesis we investigate a method for genotype representation in cellular automata. This method is inspired from gene regulation process in biology and is called self-modification. This is then combined with instruction-based approach to form SMIBA. In order to test this new method, SMIBA together with IBA and TT was tested on a number of problems relevant in artificial life. This firstly, being the problems of replication and of development, which are seen as vital for selfreplicating machines. Secondly, these two problems of replication and development are then combined into a new novel problem, which is then subsequently used to test the different methods. SMIBA was seen to perform well, in comparison to the other methods, on all problems tested. SMIBA and IBA were also shown to scale exceptionally well when incrementing maximum possible states of the CA, often even performing better. Further properties in SMIBA of delayed development and hierarchy were also identified