See Holland's work. For e.g.
Holland, J. H. (1992). Adaptation in Natural and Artificial Systems, MIT Press, Cambridge MA.
Three Elements of a Theory of Representations
Redundant Representations in Evolutionary Computation As a result, uniformly redundant representations do not change the behavior of GAs. Only by increasing r, which means overrepresenting the optimal solution, does GA performance increase. Therefore, non-uniformly redundant representations can only be used advantageously if a-priori information exists regarding the optimal solution. <> No free lunch theorem
Bias towards simplicity (see MMathPhys oral presentation) similar to regularization in Machine learning?
First about genotype-phenotype maps in general, I have found that the literature on evolutionary computation/genetic algorithms has quite a lot of good research onto the effects of GP maps in evolution.Here is an example: https://link.springer.com/article/10.1007/s10710-012-9159-4 , they call "phenotypic robustness" to what we call the phenotype's frequency, on the arrival of the frequent.
This other one (https://link.springer.com/chapter/10.1007/978-3-319-10762-2_42 ), whose conclusion is like a prelude to our current findings: "We conjecture that genotype networks could be shaped very differently in other GP systems, however our current observations capture many general properties of GP, and might even be applicable to other EC systems. Specifically, the distribution of neutrality is very heterogenous among various phenotypes. Some genotype networks, i.e. phenotypes, could be orders of magnitude larger than others. Moreover, the mutational connections among phenotypes are biased, where a phenotype has more potential to mutate to particular phenotypes and is less likely to mutate to or is even disconnected from some phenotypes. The success of an innovative evolutionary search crucially depends on locating the target phenotype, i.e. whether it is accessible from many other phenotypes, and on finding an efficient mutational path towards it. In future studies, we expect to use our methodology in other GP- or ECsystems and test if our observations and conjectures hold for a wider range of applications. It would be helpful to look into how a particular EC representation correlates with genotype network properties, such that we can gain a better understanding of how a representation influences evolutionary search and how we could improve the performance of an evolutionary algorithm by designing more appropriate representations."
This thesis ( http://etheses.whiterose.ac.uk/12035/1/thesis.pdf ), which mentions a particular bias found in Cartesian Genetic Programming, which is reminiscent of "bias towards simplicity": "However, for classification tasks, smaller solutions are often favoured over larger as they typically perform better on unseen data; mirroring the concept of Occams razor [30]. Additionally, smaller solutions are often favoured generally because (a) they are quicker to execute and (b) they are easier to understand and reason about. Finally, a bias towards certain topologies does not limit the topologies which can be found given sufficient evolutionary pressure. In this regard if a task requires a number of nodes larger or smaller than the number to which there is a bias, this is still possible. Therefore, although results were presented which showed removing length bias produced better results on problems specifically designed to require a very large percentage of the possible nodes to be active [82, 84], on many real world applications, length bias may actually be of benefit."
More significantly, a few of papers by Per Kristian Lehre, which show not only certain GP maps with bias, but explores their bias towards simplicity. He measures "phenotypic complexity" with LZW, and finds a negative correlation with "neutrality degree" (size of neutral networks): http://www.sciencedirect.com/science/article/pii/S0303264706001705 https://pdfs.semanticscholar.org/13ec/e15e53b3f6729d5f8cd79380d5dd4209d6d2.pdf http://sci-hub.cc/10.1109/eh.2005.26 I should read the second paper more carefuly, because it has plots that are similar to those showing "randomness deficit". However, he is actually looking at "genotypic complexity", and so the normal simplicity bias seems not to be there.