title: Modelling biological evolvability: implicit context and variation filtering in enzyme genetic programming.
abstract: This paper describes recent insights into the role of implicit context within the representations of evolving artefacts and specifically within the program representation used by enzyme genetic programming. Implicit context occurs within self-organising systems where a component's connectivity is both determined implicitly by its own definition and is specified in terms of the behavioural context of other components. This paper argues that implicit context is an important source of evolvability and presents experimental evidence that supports this assertion. In particular, it introduces the notion of variation filtering, suggesting that the use of implicit context within representations leads to meaningful variation filtering whereby inappropriate change is ignored and meaningful change is encouraged during evolution.
authors.affiliation: Intelligent Systems Research Group, Department of Electronics, University of York, York YO10 5DD, UK. Michael.Lones@bioinspired.com
authors.forename: Michael A
authors.initials: MA
authors.lastname: Lones
authors.forename: Andy M
authors.initials: AM
authors.lastname: Tyrrell
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D004798:Enzymes / Q000502:physiology*; D019143:Evolution, Molecular; D015971:Gene Expression Regulation, Enzymologic / Q000502:physiology*; D014644:Genetic Variation; D006801:Humans; D008957:Models, Genetic; D009154:Mutation / Q000235:genetics; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D004798:Enzymes