Automatic Generation of Caching Algorithms

Grammatical Evolution (GE) is a grammar based GA to generate computer programs which has been shown to be comparable with GP when applied to a diverse array of problems. GE has the distinction that its input is a BNF, which permits it to generate programs in any language, of arbitrary complexity, including loops, multiple line functions etc. Part of the power of GE is that it is closer to natural DNA than GP, and thus can beneet from natural phenomena such as a separation of search and solution spaces through a genotype to phenotype mapping, and a genetic code degen-eracy which can give rise to silent mutations (Mutations that have no eeect on the phenotype). In this paper we describe how GE was applied to the real world problem of evolving a Caching Algorithm, at which GP has been found to generate algorithms that did not perform as well as those designed by humans.