Tag-based regulation of modules in genetic programming improves context-dependent problem solving
This work provides a novel mechanism for evolving more dynamic genetic programs, which is significant for researchers and practitioners in genetic programming working on problems requiring context-dependent adaptation.
This paper introduces tag-based genetic regulation, a new genetic programming technique that enables programs to dynamically adjust the expression of code modules. The authors demonstrate that this technique significantly improves problem-solving performance on context-dependent problems, enabling solutions to problems that were previously unsolvable without regulation.
We introduce and experimentally demonstrate the utility of tag-based genetic regulation, a new genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible mechanism for referencing code modules. Tag-based genetic regulation extends existing tag-based naming schemes to allow programs to "promote" and "repress" code modules in order to alter expression patterns. This extension allows evolution to structure a program as a gene regulatory network where modules are regulated based on instruction executions. We demonstrate the functionality of tag-based regulation on a range of program synthesis problems. We find that tag-based regulation improves problem-solving performance on context-dependent problems; that is, problems where programs must adjust how they respond to current inputs based on prior inputs. Indeed, the system could not evolve solutions to some context-dependent problems until regulation was added. Our implementation of tag-based genetic regulation is not universally beneficial, however. We identify scenarios where the correct response to a particular input never changes, rendering tag-based regulation an unneeded functionality that can sometimes impede adaptive evolution. Tag-based genetic regulation broadens our repertoire of techniques for evolving more dynamic genetic programs and can easily be incorporated into existing tag-enabled GP systems.