Learning with the molecular-based hypernetwork model

Author

Juárez, José L Segovia ; Conrad, Michael

Author_Institution

Dept. of Comput. Sci., Wayne State Univ., Detroit, MI, USA

Volume

2

fYear

2001

fDate

2001

Firstpage

1177

Abstract

The hypernetwork model is a hierarchical architecture that has a representation of the molecular, cellular, and organismic levels of biological organization. It influences flow within each level, and through levels, forming dynamic networks of molecular interactions. With its molecular variation-selection learning algorithm, the hypernetwork is able to solve fairly complex tasks such as the (4-10)-input parity task, and the tic-tac-toe endgame problem, with good results. These performance results illustrate the learning capabilities of this model

Keywords

game theory; learning (artificial intelligence); medicine; modelling; molecular biophysics; biological organization; complex tasks; dynamic networks; hierarchical architecture; learning capabilities; molecular interactions; molecular variation-selection learning algorithm; molecular-based hypernetwork model; organismic levels; parity task; tic-tac-toe endgame problem; Biological system modeling; Biological systems; Biology computing; Computational modeling; Computer architecture; Computer science; Information processing; Nervous system; Neurons; Organisms;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on

Conference_Location

Seoul

Print_ISBN

0-7803-6657-3

Type

conf

DOI

10.1109/CEC.2001.934324

Filename

934324