A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers
Abstract
We describe a molecular evolutionary algorithm that can be implemented in DNA computing in vitro to learn the recently-proposed hypernetwork model of cognitive memory. The molecular learning process is designed to make it possible to perform wet-lab experiments using DNA molecules and bio-lab tools. We present the bio-experimental protocols for selection, amplification and mutation operators for evolving hypernetworks. We analyze the convergence properties of the molecular evolutionary algorithms on simulated DNA computers. The performance of the algorithms is demonstrated on the task of simulating the cognitive process of learning a language model from a drama corpus to identify the style of an unknown drama. We also discuss other applications of the molecular evolutionary algorithms. In addition to their feasibility in DNA computing, which opens a new horizon of in vitro evolutionary computing, the molecular evolutionary algorithm provides unique properties that are distinguished from conventional evolutionary algorithms and makes a new addition to the arsenal of tools in evolutionary computation. © 2011 IEEE.
Publication Title
2011 IEEE Congress of Evolutionary Computation, CEC 2011
Recommended Citation
Lee, J., Lee, B., Kim, J., Deaton, R., & Zhang, B. (2011). A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers. 2011 IEEE Congress of Evolutionary Computation, CEC 2011, 2735-2742. https://doi.org/10.1109/CEC.2011.5949961
