Syracuse Evolutionary and Neural Systems Exploration (SENSE) research group
WHO?
Profs. Kishan Mehrotra and
Chilukuri
K. Mohan lead the group. Current doctoral students in the research
group include Buthainah Al-kazemi (discrete particle swarm optimization),
Kamala Anupindi (financial optimization using evolutionary algorithms),
June Phansiri Athikomrungsarit (uncertainty manipulation in the context
of multiple evidence sources that supply data intermittently), S.
Kanat Bolazar (diversity in evolution strategies), Stu Card (genetic
programming and wavelet networks), and David Walter (fuzzy classifier systems
for diagnosis tasks).
Recent doctoral graduates, in reverse chronological order, include:
-
Hasan Ozdemir (evolutionary algorithms for transportation problems) [currently
at Panasonic Research Lab., Princeton]
-
Wonil Kim (adaptive multimodule approximation neural networks) [currently
at Bhasha Systems, PA]
-
Ayed Salman (adaptive linkage learning) [currently at Kuwait University]
-
Abdullah Al-Mutawa (Arabic character recognition using evolutionary algorithms)[currently
at Kuwait University]
-
Amalia Beatriz Garmendia Doval (Stack Filter Design using Evolutionary
Algorithms) [currently at Robert Gordon University, U.K.]
-
Ender Ozcan (Shape Recognition Using Evolutionary Algorithms) [currently
at Yeditepe University, Turkey]
-
Anil Menon (Replicator Systems and Majorization for Modeling Evolutionary
Algorithms) [currently at Cerebellumsoft, Seattle]
-
Harpal Maini (Knowledge-based Nonuniform Crossover) [currently at Deutschebank]
-
Chaitanya Tumuluri (Locality-Conscious Load Balancing: Connectionist Architectural
Support) [currently at Silicon Graphics]
-
Hyukjoon Lee (Neural net modeling for performance evaluation of parallel
applications) [currently teaching in Korea]
-
Ching-Tai Chiu (Fault tolerance of neural networks: analysis and algorithms)
[currently at GetSilicon.net, CA]
-
Rangachari Anand (Modular neural networks) [currently at IBM TJW Res.Cen.]
Research Interests:
We study evolutionary algorithms and artificial neural networks (see Publicationsfor
details on specific papers). MIT Press has recently published an introductory
textbook, Elements
of Neural Networks, by Profs. Mehrotra, Mohan and Ranka.
Some of our recent results are in the development of new, general-purpose
crossover operators.
Selective
Crossover performs slight perturbations in parent individuals to
compute resulting changes in parent fitness, and uses this information
to generate new offspring. Experimental results confirm that this leads
to rapid improvement in average and best fitness, when compared to 1PTX,
2PTX, and UX. For example code, please see SX.c
for a deceptive problem
(Paper appeared in the 1998 Symposium on Applied Computing (SAC'98),
February 27-March 1, 1998. More recent results are reported in IEEE-CEC,
July 1999. Current work has applied this approach to optimization problems
with significantly overlapping deceptive `building blocks'.)
Linkage
Crossover applies a probabilistic inference methodology to crossover,
with explicit representation of first order linkages between chromosomal
components. For problems in which little/no linkage information is available
a
priori, we show that a Hebbian learning rule can be applied to learn
appropriate linkages. The success of this approach is shown in 3 ways:
(i) performance (best fitness) is improved,
(ii) linkage adaptation process does converge, and
(iii) learnt linkage probabilities do correspond to expectation (for
problems whose linkage structure is known).
(Paper appeared in the 1998 Symposium on Applied Computing (SAC'98),
February 27-March 1, 1998, Atlanta (GA). More recent results are reported
in GECCO, July 1999. Extensive experiments are discussed in a forthcoming
paper in Evolutionary Computation.
In Adaptive Multi-Module Approximation Networks, we present a
new adaptive algorithm for function approximation tasks. The learning algorithm
successively introduces new (small) neural network modules, and associates
each module with a set of reference vectors identifying its most appropriate
region of applicability. This approach is especially useful in approximating
discontinuous functions and function collages; sub-functions may also be
smooth.
[Come back later to this page, I'll be adding more information soon.]
Mailing address: Prof. C.K.Mohan, 2-171 CST, Dept. of EECS, Syracuse
University, Syracuse, NY 13244-4100, USA`
Send email to ckmohan@syr.edu
or visit the web-page
of Chilukuri K. Mohan