Name
Abstract | ||
|---|---|---|
In this paper, we introduce our approach for evolving reaction networks. It is an efficient derivative of the neuroevolution of augmenting topologies algorithm directed at the evolution of biochemical systems or molecular programs. Our method addresses the problem of meaningful crossovers between two chemical reaction networks of different topologies. It also builds on features such as speciation to speed up the search, to the point where it can deal with complete, realistic mathematical models of the biochemical processes. We demonstrate this framework by evolving credible biochemical answers to challenging autonomous molecular problems: in vitro batch oscillatory networks that match specific oscillation shapes. Our experimental results suggest that the search space is efficiently covered and that, by using crossover and preserving topological innovations, significant improvements in performance can be obtained for the automatic design of molecular programs. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TEVC.2014.2326863 | Evolutionary Computation, IEEE Transactions |
Keywords | Field | DocType |
biochemical oscillators,evolutionary algorithm (ea),molecular programming,encoding,mathematical models,molecular biophysics,genetics,topology,neuroevolution,in vitro,chemicals,mathematical model,biochemistry | Crossover,Effective method,Network topology,Neuroevolution of augmenting topologies,Artificial intelligence,Mathematical model,Mathematics,Machine learning,Speedup | Journal |
Volume | Issue | ISSN |
19 | 3 | 1089-778X |
Citations | PageRank | References |
8 | 0.61 | 12 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huy Q. Dinh | 1 | 8 | 0.61 |
| Nathanael Aubert | 2 | 17 | 4.36 |
| Nasimul Noman | 3 | 323 | 21.61 |
| Teruo Fujii | 4 | 182 | 39.22 |
| Yannick Rondelez | 5 | 10 | 2.39 |
| Hitoshi Iba | 6 | 1541 | 138.51 |