From: ylkingsb@icr.uwaterloo.ca ()
Subject: ICR Presents a Colloquium on "Hybrid-Genetic/Gradient Learning in Multi-Layer Neural Networks", Wednesday, November 25, 1992 at 3:30 pm in DC 1302.
Keywords: Speaker: Dr. Mohamad H. Hassoun, Computation and Neural Network Lab.,  Dept. of Elec. and Comp. Eng., Wayne State Univ., Detroit, Michigan.
Organization: University of Waterloo
Distribution: ont
Date: Tue, 17 Nov 1992 16:19:03 GMT



                   The University of Waterloo
                      200 University Avenue
                        Waterloo, Ontario


           The Institute for Computer Research (ICR)

                    Presents a Colloquium on

 Hybrid Genetic/Gradient Learning in Multi-Layer Neural Networks



by:     Dr. Mohamad H. Hassoun


of:     Computation and Neural Network Laboratory
        Department of Electrical and Computer Engineering
        Wayne State University
        Detroit, Michigan



Date:   Wednesday, November 25, 1992  
Time:	3:30 p.m.
Place:  William G. Davis Computer Research Centre, Room 1302


Abstract

Learning in neural networks is a  form  of  search  in  a  multi-
dimensional  space.   The  popular backpropagation learning algo-
rithm for layered neural networks represents  a  class  of  local
gradient-based search.  The optimality of gradient search in com-
plex spaces (e.g., neural net weight spaces) is  constrained  due
to  local  minimas.   On the other hand, global search strategies
(simulated annealing, genetic algorithms, etc.) lead  to  optimal
solutions   but   at   the  expense  of  increased  computational
complexity/search time.  In this presentation, a  novel  learning
technique  is proposed for multi-layer neural networks based on a
hybrid genetic search in hidden target space/gradient  search  in
weigh space strategy.  Several versions of the proposed algorithm
are evaluated on benchmark problems with varying complexity.  Our
simulations  show  that the new algorithm combines the global op-
timization capabilities of genetic algorithms with the  speed  of
gradient  descent  search, for increased learning efficiency.  In
addition, we show that genetic search in hidden target  space  is
less complex than that in weight space.

Biography:

Mohamad H. Hassoun was born in Lebanon, in 1961.  He received the
B.S.,  M.S.,  and  Ph.D.  degrees  in electrical engineering from
Wayne State University (WSU), Detroit, Michigan, in  1981,  1982,
and  1986,  respectively.  He is currently as Associate Professor
in the Department of Electrical and Computer Engineering at Wayne
State University.  Dr. Hassoun's research interests are in artif-
icial neural systems, parallel collective optimization and compu-
tations,  learning  algorithms, and optical and electronic neural
network implementations.

Dr. Hassoun has received a National Science  Foundation  Research
Initiation  Award  and a Presidential Young Investigator Award in
1988 and 1990, respectively.  He is presently the associate  edi-
tor for book reviews and a technical associate editor of the IEEE
Transactions on Neural Networks.  He currently serves as  program
committee  member  for  the 1993 IEEE International Conference on
Neural Networks - ICNN (San Francisco, CA, March  1993)  and  the
International  Workshop  on  Artificial  Neural  Networks - IWANN
(Spain, June 1993).  He has published over 35 journal and confer-
ence  proceedings  papers on the subject of neural networks.  Dr.
Hassoun is a member of IEEE, INNS, SPIE, Sigma Xi, ASEE, and  Tau
Beta Pi.



Everyone is welcome.  Refreshments served.