Prerequisites: CS 302, MATH 330
Syllabus
CS 457: Computational Intelligence
Winter 2009
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Meeting Times
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Lect: 1:00 - 1:50
MWF, HB 106
Labs:
1:00 - 1:50 Tuesday, HB 203
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Instructor
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Dr.
Razvan Andonie, HB 219-B, Office hours
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TA
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Texts
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Russell C.E., Yuhui S. Computational Intelligence Concepts to
Implementations, Elsevier, 2007.
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Objectives
Introducing
concepts, models, algorithms, and tools for development of intelligent systems.
Example topics include artificial neural networks, genetic algorithms, fuzzy systems, swarm
intelligence, ant colony optimization, artificial life, and hybridizations of
the above techniques. This domain is called Computational Intelligence, and it
is a numerical interpretation of biological intelligence.
Topics:
Mimicking
Nature for Problem Solving: The Basic Concepts
Single-Layer and
Multi-Layer Feedforward Neural Networks
Feedback Neural
Networks
Associative
Memories
Learning Vector Quantizer (LVQ)
Self-Organizing
Feature Maps
Radial Basis
Function Neural Networks
Support Vector
Machines
Genetic
Algorithms
Fuzzy Sets and
Fuzzy Logic
Fuzzy Neural
Networks
Fuzzy ARTMAP
Swarm
Intelligence and Ant Colony Optimization
Feature
Selection
The Power and
Computational Complexity of Computational Intelligence Models
Student Learning Outcomes
On
completion of this course, the student will have:
An understanding of the fundamental
Computational Intelligence models
Implemented neural networks, genetic
algorithms, fuzzy neural networks, and ant colony optimization algorithms.
Applied Computational Intelligence
techniques to classification, pattern recognition, prediction, rule extraction,
and optimization problems.
Grading
The will be no
exams. Grading will be based only on attendance, assignments, in-class
presentations, and the Final Project:
10% Class Participation
40% Assignments and Presentations
50% Final Project
The
final project is to be completed in groups of two, or individually. It should
be submitted as a report and presented in class. A project progress
report (1/2 page) should be submitted periodically (see Course Schedule). The project
should contain the problem description, the code, the benchmarking, and a
discussion. For a good presentation, read Quinn's effective
presentation techniques guidelines.
Grade Distribution:
95 - 100 A
90 - 94 A-
87 - 89 B+
83 - 86 B
80 - 82 B-
77 - 79 C+
73 - 76 C
70 - 72 C-
67 - 69 D+
63 - 66 D
60 - 62 D-
0 -
59 F
Current final projects
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NOTE: The projects may be
continued: CS496 Individual Study (up to 4 credits) or CS458 AI Project (2
credits).
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Final
projects from previous years
2008/09
Integrating the neuro-genetic
CWU Chess engine in the ARENA GUI – Kyle Littlefield
Society and Civilization: Simulation of Human Behavior – Martin Olevnik
Expert system Asteroids - Kasey Loomis
A GA approach for adaptive radiation - Wenbin Mei
Sudoku solution via evolutionary computation – Eugen
Roseberry
2007/08
Ant Colony Optimization for
Graph Coloring - Brandon Wysocki,
Benjamin Sisson
Ordered Fuzzy ARTMAP for Predicting HIV-1 Protease
Inhibitors - Christopher B. Abdul-Wahid, Grant
I. Barker
Genetic algorithms in investment decision
making modeling - Michael Wilson, Steven Curtis
Attack detection in computer networks using a neural
network approach - Hsun-Hung
Chien
A Genetic Approach for Crowd Simulation - Kevin Venables,
Jason White
2006/07
Data Clustering with Particle
Swarms - Jarrett Sauby
SexualGA: Gender-Specific Selection for
Genetic Algorithms - Robb Berry
User Authentication using Backpropagation - Ted Steffen
Computational Intelligence Techniques for Solving Rubik’s
Cube - Joel Giese, Berk
Erkul
Modeling Civil Violence: An Evolutionary Multi-Agent Approach - Charles Stahl, Abe Dybvig
A Neural Network Application for Attack Detection in
Computer Networks - Toby Lewandoski
A Self-Organizing Map Approach for Clustering of XML
Documents - Sorin
Bucse
2005/06
Web Data Mining - Jessica and Aaron Berglund
Fuzzy ARTMAP Function Learning for Chess - Luke Magill
Ant Colony Optimization Applied to the Seega
Game - Andrew Szydlowski, Andrew Howell
CWU Monsters Game - Luke Arntson, Loren Harris
Code Breaking - Chris Lee
Language Processor - Blaire Sherman
Password Authentication using Backpropagation
- Jonathan Widger
2004/05
Computational
Chemistry - Sarah Abdul-Wahid,
Catharine Collar
Voice Recognition - Virgil Mednick
Email Data Mining - Robert Mitchell
Swarm Theory and Enemy Armor - Eric Newhouse
Optimization of Buffer Sizes in
Asynchronous Assembly - Yu-chih
Hsiao
2003/04
Music
Notation Convertor: Andreas Stefik
Swarm Intelligence in Checker: Kelly McBride, Tye
Wick
Neural Networks in Chess: Ashur Odah, Joe Lemley, Phil Leder, Pushpinder Heer
Spam Filtering using Computational
Intelligence Techniques: Aaron St. John, Cory Stuart, Michael
West
Japanese Kanji Character
Recognition: Andrew Pickard,
Benjamin Rooney
Customer Satisfaction Measuring
using Neural Networks: Logan Riggs, Tom Rockey, Michael Pankiewcz
Lectures
The
slides for lectures and additional material can be found in the shared
directory on Neve.
Software
The
source code of the textbook is written in C and C++ and is shareware. It can be
downloaded from http://www.computelligence.org/issue/CICI/CICI.html.
You are welcome to use it for classroom or personal learning. Here is the list
of the software modules:
Genetic algorithm
Particle swarm optimization
Back-propagation neural network
Learning vector quantization neural
network
Self-organizing feature map neural
network
Evolutionary back-propagation neural
network
Fuzzy rule system
Evolutionary fuzzy rule system
Fuzzy evolutionary fuzzy rule system
Neural network explanation facility
Free
software can be also found at:
Stuttgart Neural Network Simulator
CIlib
(Computational Intelligence Library) is a Java framework for developing
Computational Intelligence software (University of Pretoria, South Africa).
The Java Object Oriented Neural Network (JOONE)
is an open source project that offers a highly adaptable neural network for
Java programmers. The source code is freely available. JOONE can allow you to
create neural networks easily from a Java program. JOONE supports many
features, such as multithreading and distributed processing. This means that
JOONE can take advantage of multiprocessor computers and multiple computers to
distribute the processing load.
ECG 11: A Java-based
Evolutionary Computation and Genetic Programming Research System (George Mason
University).
ARTMAP
software (Boston University)
LIBSVM -- A Library for
Support Vector Machines
Additional
Reading
Zurada, J. Introduction
to Artificial Neural Systems. West Publishing Company, St. Paul, 1992. I
have used this textbook for several years in Brasov (Romania).
Haykin, S. Neural
Networks - A Comprehensive Foundation. Macmillan College Publishing
Company, New York, 1999 (second edition). It is generally considered as one of
the best.
de Castro, L.N. Fundamentals
of Natural Computing. Chapman & Hall/CRC, 2006. Presents a wide-ranging
survey of techniques of nature-inspired computing.
Engelbrecht, A.P. Fundamentals
of Computational Swarm Intelligence, Wiley, 2005. A good overview of
evolutionary computation, particle swarm optimization (PSO), and ant
algorithms, with their connections to optimization theory.
Engelbrecht, A.P. Computational Intelligence: An
Introduction, Wiley, NY, 2007, second edition.
Arguably, the best compendium on computational intelligence, but not a good
textbook.
Jones, M. T. AI
Application Programming, second edition, Charles River Media, Hingham,
Massachusetts, 2005. It has some useful implementations.
Links
Gurney's Notes help you to
learn about the basic concepts and ideas in artificial neural networks.
DSI Neural Networks Group is a complete
source of electronic information
Genetic Algorithm
Tutorial
- this site gives a short introduction to
genetic algorithms.
Machine Learning Datasets is a very good
source of benchmarks for testing your neural implementations.
Ant Colony Optimization is a source of
tutorials.
SwarmWiki, hosted by the
Center for the Study of Complex Systems at the University of Michigan.
Particle swarm optimization (PSO)
is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social
behavior of bird flocking or fish schooling.
A Brief History of
Connectionism by D. A. Medler, Neural Computing
Surveys, 1, 1999, 61-101.
IEEE Computational Intelligence Society
Neural Computing Research Programs
Worldwide
Fuzzy Sets and Systems,
a nice collection of resources from Robert Fuller.
Neural Java: Neural Networks Tutorial
with Java Applets is a series
of exercises and demos. Each exercise consists of a short introduction, a small
demonstration program written in Java (Java Applet), and a series of questions
which are intended as an invitation to play with the programs and explore the
possibilities of different algorithms. You can download the source code of the
applets.
Multiagents, contains many links on
intelligent agents.
Students
with disabilities who wish to set up academic adjustment in this class should
give me a copy of their "Confirmation of Eligibility for Academic
Adjustment" from the Disability Support Services Office as soon as
possible so we can discuss how the approved adjustment will be implemented in
this class. Students without this form should contact the Disability Support
Services Office, Buillon 205 or dssrecept@cwu.edu or 963-2171.
The
schedule and procedures for this course are subject to change. It is the
student's responsibility to learn of and adjust to changes.