A molecular evolutionary algorithm for learning hypernetworks on simulated DNA computers

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.

[1]  K. Pagiamtzis,et al.  Content-addressable memory (CAM) circuits and architectures: a tutorial and survey , 2006, IEEE Journal of Solid-State Circuits.

[2]  Byoung-Tak Zhang,et al.  Text Classifiers Evolved on a Simulated DNA Computer , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[3]  David E. Rumelhart,et al.  The architecture of mind: a connectionist approach , 1989 .

[4]  Byoung-Tak Zhang,et al.  Evolutionary hypernetwork classifiers for protein-proteininteraction sentence filtering , 2009, GECCO '09.

[5]  L M Adleman,et al.  Molecular computation of solutions to combinatorial problems. , 1994, Science.

[6]  Byoung-Tak Zhang,et al.  Evolving hypernetwork classifiers for microRNA expression profile analysis , 2007, 2007 IEEE Congress on Evolutionary Computation.

[7]  Byoung-Tak Zhang,et al.  Molecular programming: evolving genetic programs in a test tube , 2005, GECCO '05.

[8]  Milan N. Stojanovic.  Two Molecular Information Processing Systems Based on Catalytic Nucleic Acids , 2009, IWNC.

[9]  Byoung-Tak Zhang,et al.  Layered Hypernetwork Models for Cross-Modal Associative Text and Image Keyword Generation in Multimodal Information Retrieval , 2010, PRICAI.

[10]  加藤 隆二,et al.  A Content Addressable Distributed Logic Memory with Applications to Information Retrieval , 1963 .

[11]  Ehud Shapiro,et al.  Molecular implementation of simple logic programs. , 2009, Nature nanotechnology.

[12]  Geoffrey E. Hinton,et al.  Distributed Representations , 1986, The Philosophy of Artificial Intelligence.

[13]  Byoung-Tak Zhang,et al.  An evolutionary Monte Carlo algorithm for predicting DNA hybridization , 2008, Biosyst..

[14]  Byoung-Tak Zhang,et al.  Hypernetworks: A Molecular Evolutionary Architecture for Cognitive Learning and Memory , 2008, IEEE Computational Intelligence Magazine.

[15]  Teuvo Kohonen,et al.  Content-addressable memories , 1980 .

[16]  Junghuei Chen,et al.  A DNA-based memory with in vitro learning and associative recall , 2003, Natural Computing.

[17]  Byoung-Tak Zhang,et al.  Evolving hypernetworks for pattern classification , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Erik Winfree,et al.  An information-bearing seed for nucleating algorithmic self-assembly , 2009, Proceedings of the National Academy of Sciences.

[19]  Byoung-Tak Zhang,et al.  Self-Assembling Hypernetworks for Cognitive Learning of Linguistic Memory , 2008 .

[20]  R. Levine,et al.  DNA computing circuits using libraries of DNAzyme subunits. , 2010, Nature nanotechnology.

[21]  Chen-Yi Lee,et al.  High-throughput data compressor designs using content addressable memory , 1994, Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94.

[22]  E B Baum,et al.  Building an associative memory vastly larger than the brain. , 1995, Science.