The above is the title of my paper that has been accepted by IEEE Trans. on Evolutionary Computing. My doctoral supervisor Dr Kaur is a coauthor of the paper.
Here is the abstract:
We present a new perspective of search in Evolutionary Computing (genotype to phenotype maps. The model groups genes into quotient sets and shows their adjacencies. A unique quality of the quotient model is that it details geometric qualities of maps that are not otherwise easy to observe. The model shows how random mutations on genes make non-random phenotype preferences, based on the structure of a map. The interaction between such mutation-based preferences with fitness preferences is important for explaining population movements on neutral landscapes. We show the widespread applicability of our approach by applying it to different representations, encodings and problems including Grammatical Evolution (GE), Cartesian Genetic Programming, Parity and Majority Coding, OneMax, Needle-in-Haystack, Deceptive Trap and Hierarchical if-and-only-if. We also use the approach to address conflicting results in the neutral evolution literature and to analyze concepts relevant to neutral evolution including robustness, evolvability, tunneling and the relation between genetic form and .) by using a novel model for the analysis and visualization of
We use the model to develop theoretical results on how mapping and neutral evolution affects search in GE. We study the two phases of mapping in GE; these being transcription (i.e. unique identification of genes with integers), and equivalence classes, therefore the properties we derive for specific schemes are applicable to classes of schemes. We present a new perspective on population diversity. We specify conditions under which increasing degeneracy (by increasing codon size) or rearranging the rules of a grammar do not affect performance. It is shown that there is a barrier to nontrivial neutral evolution with the use of the natural transcription with modulo translation combination; a necessary but not for such evolution is at least three bits should change on mutation within a single codon. This barrier can be avoided by using Gray transcription. We empirically validate some findings.(i.e. many-to-one mapping of genotypes to phenotypes). It is shown that translation and transcription schemes belong to
This paper was originally written with a more modest scope limited to Grammatical Evolution (which was a central part of my dissertation). If it seems overachieving it is partly due to me and partly due to reviewer requests for a more general approach. The amount of work behind this paper is painful to even think of. I am not complaining though, the final product is certainly worth the effort and I am very satisfied at the quality.
Please email me if you want to see a draft copy.