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\footnotesize \sf
Computer Science and Engineering
\\
Ferguson Hall -- 0115
\\
472-2402
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\LARGE \bf
Course Specification\vspace*{3mm}
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{\Large \bf \begin{center} CSCE-479\\
Neural Networks and Genetic Algorithms
\end{center} }

\paragraph{Catalogue Description:}~ \newline
479. Neural Networks and Genetic Algorithms (3 cr).
This
introduction to the concepts, design and application of
connection-based computing begins by simulating neural networks.  The
focus will be on competing alternative network architectures,
including sparse distributed memories, Hopfield networks, and the
multilayered feed-forward systems.  The construction and improvement
of algorithms used for training of neural networks will be addressed
to reduce training time and improve generalization.  Algorithms for
training and synthesizing effective networks are implemented in high
level language programs running on conventional computers.  There will
be an emphasis on methods for synthesizing and simplifying network
architectures for improved generalization.  Application areas treated
include: pattern recognition, computer vision, robotics medical
diagnosis, weather and economic forecasting.



\paragraph{Textbook(s) and/or Other Required Materials:}~ \newline
S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd Ed.,
Prentice Hall, 1999. \\
D.  Goldberg, Genetic Algorithms in Search, Optimization, and Machine
Learning, Reading. MA Addision Wesley.


\paragraph{Prerequisites by Topic:}~ 
\begin{enumerate}
\item {\bf Mastery} of competence in programming for one high level
 computer language,
 mathematical maturity competence to the level of introductory calculus.
\item {\bf Familiarity} with basic algebra, data structures, program
  organization, pseudo-code, objectives of intelligent systems.
\item {\bf Exposure} to Programming Languages, Alternative styles of program
construction Recursion, World wide web and personal computer based level
of smart programs.
\end{enumerate}

\paragraph{Course Objectives:}~ 
\begin{enumerate}
\item {\bf Mastery} Understanding of the paradigm shift from procedural
 algorithmic
 problem solution to connectionist, subsymbolic approaches, supervised
 and self-organizing neural network model architectures, implementation
 of one form of neural network and also of the basic model of
 fixed-length, chromosome-based genetic algorithms and operator
 definitions.
\item {\bf Familiarity} with alternative architectures for neural networks and
evolutionary systems. \\
Familiarity with alternative supervised and self-organization algorithms
for neural networks and evolutionary systems. \\
Familiarity with practical methods for formulating complex problems for
solution by connectionist or evolutionary paradigms. \\
Familiarity with data from real world problems and under controlled
conditions synthesizing it for solving toy problems and validating
neural networks of full scale problems.
\end{enumerate}

\vfill
{\small ~ \hfill  \today}
\newpage
\paragraph{Topics Covered:}~ \newline
Arithmetic and Logic Level. \\
Historical background: adalines, madalines, sparse distributed memories,
Hebbian learning and the delta rule, backpropagation learning for the
PDP model, adaptive resonance theory for networks, counter propagation,
self-organizing maps, cognitron and neocognitron \\
Supervised and unsupervised training: principles and parameters, data
and training pattern structure and sequencing, principles of operation. \\
Organization of problem solving systems variety of neural network
models,achitectures and traiing and self organizing methods. \\
Evolutionary Problem Solving Paradigms: fundamental theorem of genetic
algorithms, Introduction to evolutionary computing with the basic
genetic algorithm and genetic programming. \\
Schema Theorem and Genetic Operators: crossover, mutation and reversal
or inversion, Fitness and selection pressure, Sequential and Parallel
implementations. \\
Artificial life and application areas for genetic algorithms and
programming.


\paragraph{Relationship of Course to Program Objectives:}~ \newline
\underline{{\bf Ignore for Fall 1998}. Details will 
be firmed up during Spring 1998 Semester.}


\paragraph{Contribution of Course to Meeting the Professional Component:}~ \newline
\underline{{\bf Ignore for Fall 1998}. Details will 
be firmed up during Spring 1998 Semester.}


\paragraph{Class/Laboratory Schedule:}~ \newline
Lecture: 45 hours = 3 hours/week for 15 weeks


\paragraph{Prepared by:}~ \newline
 A.J. Surkan, Professor

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