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CE 563:
Systems Optimization Using Evolutionary Algorithms
In support of his
associations with the Operations Research and Institute of Computational
Science programs, Dr. Reed has developed a new course entitled CE 563
Systems Optimization using Evolutionary Algorithms, which has had
students from several different departments within the College of
Engineering. The course emphasizes state-of-the-art methods for
designing and implementing evolutionary algorithms for solving
computationally intensive engineering and science problems. Students
are required to effectively communicate the results of their original
research in conference style papers. Example project reports can be
accessed below.
Course Description:
Evolutionary algorithms (EAs) are global
optimization heuristics that search for optima using a process that is
analogous to Darwinian natural selection. Since their inception in the
1960s, evolutionary algorithms have been used in a tremendous array of
applications. The growing popularity of evolutionary algorithms stems
from their ease of implementation and robust performance for difficult
engineering and science problems.
This course provides a comprehensive
introduction to the field of genetic and evolutionary computation (GEC).
The course will emphasize state-of-the-art methods for designing and
implementing evolutionary algorithms for computationally intensive
engineering and science problems. Course concepts are demonstrated using
case studies drawn from the disciplines of the students enrolled.
Course Syllabus
Projects Portfolio:
2007 Reports
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Optimal
Gait Analysis of Snake Robot Dynamics
Author(s): V. Mehta
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Optimal
Triangular Lagrange Point Insertion Using Lunar Gravity Assist
Author(s): J. Benavides, E. Davis, B. Wadsley
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Comparison
of Evolutionary Algorithms on the Minimax Sensor Location Problem
Author(s): W. Conner
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A Multiple
Population Differential Evolution Sampler for Trade Space
Visualization
Author(s): D. Carlsen, C. Congdon
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A Hybrid
Multi-objective Genetic Algorithm for Topology Optimization
Author(s): K. Olympio
2006 Reports
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Optimal
Low-Thrust Rendezvous using Hybrid Evolutionary Strategy
Author(s): C. Scott, D. Brown, P. Cipollo
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Crossing
over Evolutionary Algorithms
Author(s): E. Bentivegna
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Evolutionary Designs for Robust Parameter Design Experimentation
Author(s): E. Santiago
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Combinatorial Source Inversion from Displacement and Tilt
Measurements at Soufriere Hills Volcano
Author(s): J. Taron
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Multiobjective Optimization of Low Impact Development Scenarios in
an Urbanizing Watershed
Author(s): G. Zhang
2005 Reports
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Optimal Space
Trajectory Design: A Heuristic-Based Approach
Author(s): C. R. Bessette
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The XCS Classifier System in a Financial Market
Author(s): E. K. Boland, K. R. Klingebiel, T. R. Stodgell
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Investigating the
Application of Genetic Programming to Function Approximation
Author(s): J. E. Emch
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Ultrasonic Sensor
Placement Optimization in Structural Health Monitoring
Author(s): H. Gao (download
Movie of
Sensor Placement Evolution)
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Phased Linear
Stochastic Array Synthesis via Hybrid Particle Swarm Optimization
Author(s): Z. Bayraktar
2004 Reports
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Airfoil
Shape Optimization using Evolutionary Algorithms
Author(s): E. Alpman
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A Smart Model for Welding Engineers
to Achieve Target Fusion Zone
Geometry and Microstructure using Parallel Genetic Algorithm
Author(s): A. Kumar, S. Mishra
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Evolving Game Strategies to Minimize Power Consumption in Agent
Based Multi-Hop Wireless Networks
Author(s):
M. Udaiyanathan, A. Kaul
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Supply Chain Optimization using Multi-Objective Evolutionary
Algorithms
Author(s): E.G. Pinto
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Interplanetary Trajectory Optimization using a Genetic Algorithm
Author(s) : A. Weeks
2003 Reports
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Feature Selection for Classification of Hyperspectral Remotely
Sensed data using NSGA-II.
Author(s): M. Kumar
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Application of Genetic Algorithms To Vehicle Suspension Design
Author(s): H. Yu, N. Yu
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Clustering of Activity Patterns Using Genetic Algorithms
Author(s): O. Pribyl
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Multiobjective
Genetic Algorithm for Product Design
Author(s): J. Nanda
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Hybridized arrival time control approach to JIT job-shop scheduling
Author(s) : N.I. Shaikh, V.V. Prabhu,
P.M. Reed
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