Accession Number : ADA268680


Title :   High-Level Connectionist Models


Descriptive Note : Semi-Annual rept.,


Corporate Author : OHIO STATE UNIV COLUMBUS DEPT OF COMPUTER AND INFORMATION SCIENCE


Personal Author(s) : Pollack, Jordan B


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a268680.pdf


Report Date : Apr 1993


Pagination or Media Count : 53


Abstract : Standard methods for inducing both the structure and weight values of recurrent neural networks fit an assumed class of architectures to every task. This simplification is necessary because the interactions between network structure and function are not well understood. Evolutionary computation, which includes genetic algorithms and evolutionary programming, is a population-based search method that has shown promise in such complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. This algorithm's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.


Descriptors :   *NEURAL NETS , *NETWORKS , *HIGH LEVEL LANGUAGES , *ARCHITECTURE , ALGORITHMS , COMPUTATIONS , COMPUTER PROGRAMMING , POPULATION , STANDARDS , INFORMATION PROCESSING , PAPER , GENETICS , SIMPLIFICATION , VALUE , WEIGHT , STRUCTURES , INTERACTIONS , ACQUISITION , FUNCTIONS


Subject Categories : Computer Programming and Software


Distribution Statement : APPROVED FOR PUBLIC RELEASE