Projects per year
Abstract
The artifcial epigenetic network (AEN) is a computational
model which is able to topologically modify its structure according to
environmental stimulus. This approach is inspired by the functionality
of epigenetics in nature, specically, processes such as chromatin modifications which are able to dynamically modify the topology of gene
regulatory networks. The AEN has previously been shown to perform
well when applied to tasks which require a range of dynamical behaviors
to be solved optimally. In addition, it has been shown that pruning of
the AEN to remove non-functional elements can result in highly com-
pact solutions to complex dynamical tasks. In this work, a method has
been developed which provides the AEN with the ability to self prune
throughout the optimisation process, whilst maintaining functionality.
To test this hypothesis, the AEN is applied to a range of dynamical
tasks and the most optimal solutions are analysed in terms of function
and structure.
model which is able to topologically modify its structure according to
environmental stimulus. This approach is inspired by the functionality
of epigenetics in nature, specically, processes such as chromatin modifications which are able to dynamically modify the topology of gene
regulatory networks. The AEN has previously been shown to perform
well when applied to tasks which require a range of dynamical behaviors
to be solved optimally. In addition, it has been shown that pruning of
the AEN to remove non-functional elements can result in highly com-
pact solutions to complex dynamical tasks. In this work, a method has
been developed which provides the AEN with the ability to self prune
throughout the optimisation process, whilst maintaining functionality.
To test this hypothesis, the AEN is applied to a range of dynamical
tasks and the most optimal solutions are analysed in terms of function
and structure.
| Original language | English |
|---|---|
| Title of host publication | Information Processing in Cells and Tissues |
| Subtitle of host publication | 10th International Conference, IPCAT 2015, San Diego, CA, USA, September 14-16, 2015, Proceedings |
| Editors | Michael Lones, Andy Tyrrell, Stephen Smith, Gary Fogel |
| Publisher | Springer |
| Pages | 153-165 |
| ISBN (Electronic) | 978-3-319-23108-2 |
| ISBN (Print) | 978-3-319-23107-5 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | 10th International Conference on Information Processing in Cells and Tissues (IPCAT 2015) - CA, San Diego, United States Duration: 14 Sept 2015 → 16 Sept 2015 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 9303 |
| ISSN (Print) | 0302-9743 |
Conference
| Conference | 10th International Conference on Information Processing in Cells and Tissues (IPCAT 2015) |
|---|---|
| Country/Territory | United States |
| City | San Diego |
| Period | 14/09/15 → 16/09/15 |
Projects
- 1 Finished
-
Bio-inspired Adaptive Architectures and Systems
Tyrrell, A., Dunn, K., Tempesti, G., Timmis, J., Trefzer, M. A. & Turner, A. P.
28/02/14 → 31/08/19
Project: Research project (funded) › Research
Datasets
-
Population fitness and and sizes for the Artificial Epigenetic Network controllers
Turner, A. P. (Creator), Tyrrell, A. (Supervisor), Trefzer, M. (Supervisor) & Lones, M. A. (Supervisor), University of York, 14 Sept 2015
DOI: 10.15124/3f245e80-c306-4ada-8920-a0282e4962b3
Dataset