Neutral genetic drift: an investigation using Cartesian Genetic Programming

Abstract

Neutral genetic drift is an evolutionary mechanism which can strongly aid the escape from local optima. This makes neutral genetic drift an increasingly important property of Evolutionary Computational methods as more challenging applications are approached. Cartesian Genetic Programming (CGP) is a Genetic Programming technique which contains explicit, as well as the more common implicit, genetic redundancy. As explicit genetic redundancy is easily identified and manipulated it represents a useful tool for investigating neutral genetic drift. The contributions of this paper are as follows. Firstly the paper presents a substantial evaluation of the role and benefits of neutral genetic drift in CGP. Here it is shown that the benefits of explicit genetic redundancy are additive to the benefits of implicit genetic redundancy. This is significant as it indicates that that levels of implicit genetic redundancy present in other Evolutionary Computational methods may be insufficient to fully utilise neutral genetic drift. It is also shown than the identification and manipulation of explicit genetic redundancy is far easier than for implicit genetic redundancy. This is significant as it makes the investigations here possible and leads to new possibilities for allowing more effective use of neutral genetic drift. This is the case not only for CGP, but many other Evolutionary Computational methods which contain explicit genetic redundancy. Finally, it is also shown that neutral genetic drift has additional benefits other than aiding the escape from local optima.

Coexistence of complete competitors with fitness inequality

“…The obtained results demonstrate that the smaller the size of the lattice, the more stochastic fluctuations of competition outcomes appear in favor of one of the species. This individual-based model reproduces a phenomenon of the neutral genetic drift (Turner and Miller, 2015), based on stochastic fluctuations similar to the Moran Process (Moran, 1958). In small ecosystems, even in the absence of a selection, stochastic fluctuations may drive one of the species to extinction.…”

Recurrent Cartesian Genetic Programming of Artificial Neural Networks

“…Overestimating the number of available nodes has shown to greatly aid evolution [45,63]; which is thought to heighten neutral genetic drift but could also be compensating for length bias [18,19]. The reason it is thought that such a simple evolutionary strategy is so effective for CGP is twofold.…”

“…Firstly, CGP does not typically utilise crossover and so there is no requirement to maintain genetic diversity. Secondly, the reason this does not not lead to CGP easily becoming trapped in local optima is due to the inactive genes creating plateaus in the search space which are navigated across via neutral genetic drift [63,73].…”

Evolving graphs with semantic neutral drift

“…Neutral drift, a mechanism whereby individuals with fitness-equivalent phenotypes to the existing population may be generated by mutation (Galván-López et al 2011) offers the search of new neighborhoods for sampling thus increasing the chance of leaving local optima. A number of studies on neutrality in Cartesian Genetic Programming (CGP) (Miller and Smith 2006;Vassilev and Miller 2000;Turner and Miller 2015b) find it to be an almost always beneficial property for studied problems. In general, comparative studies (Miller 2011) find that CGP using only mutation and neutral drift is able to compete with traditional treebased Genetic Programming (GP) which uses more familiar crossover operators (see Koza 1992) to introduce genetic variation.…”

“…In general, comparative studies (Miller 2011) find that CGP using only mutation and neutral drift is able to compete with traditional treebased Genetic Programming (GP) which uses more familiar crossover operators (see Koza 1992) to introduce genetic variation. Turner and Miller (2015b) makes a distinction between implicit neutral drift (where a genetic operator yields a semantically equivalent child) and explicit neutral drift (where a genetic operator only modifies intronic code). We note that many comparative studies largely focus on the role of both types of neutral drift as byproducts of existing genetic operators and neutrality within the representation (Miller and Smith 2006;Vassilev and Miller 2000;Turner and Miller 2015b;Banzhaf 1994) rather than as deliberately designed features of an evolutionary system.…”