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

  1. Optimal Gait Analysis of Snake Robot Dynamics
    Author(s): V. Mehta

  2. Optimal Triangular Lagrange Point Insertion Using Lunar Gravity Assist
    Author(s): J. Benavides, E. Davis, B. Wadsley

  3. Comparison of Evolutionary Algorithms on the Minimax Sensor Location Problem
    Author(s): W. Conner

  4. A Multiple Population Differential Evolution Sampler for Trade Space Visualization
    Author(s): D. Carlsen, C. Congdon

  5. A Hybrid Multi-objective Genetic Algorithm for Topology Optimization
    Author(s): K. Olympio


2006 Reports

  1. Optimal Low-Thrust Rendezvous using Hybrid Evolutionary Strategy
    Author(s): C. Scott, D. Brown, P. Cipollo

  2. Crossing over Evolutionary Algorithms
    Author(s): E. Bentivegna

  3. Evolutionary Designs for Robust Parameter Design Experimentation
    Author(s): E. Santiago

  4. Combinatorial Source Inversion from Displacement and Tilt Measurements at Soufriere Hills Volcano
    Author(s): J. Taron

  5. Multiobjective Optimization of Low Impact Development Scenarios in an Urbanizing Watershed
    Author(s): G. Zhang


2005 Reports

  1. Optimal Space Trajectory Design: A Heuristic-Based Approach
    Author(s): C. R. Bessette

  2. The XCS Classifier System in a Financial Market
    Author(s): E. K. Boland, K. R. Klingebiel, T. R. Stodgell

  3. Investigating the Application of Genetic Programming to Function Approximation
    Author(s): J. E. Emch

  4. Ultrasonic Sensor Placement Optimization in Structural Health Monitoring
    Author(s): H. Gao (download Movie of Sensor Placement Evolution)

  5. Phased Linear Stochastic Array Synthesis via Hybrid Particle Swarm Optimization
    Author(s): Z. Bayraktar

2004 Reports

  1. Airfoil Shape Optimization using Evolutionary Algorithms
    Author(s): E. Alpman

  2. A Smart Model for Welding Engineers to Achieve Target Fusion Zone Geometry and Microstructure using Parallel Genetic Algorithm
    Author(s): A. Kumar, S. Mishra

  3. Evolving Game Strategies to Minimize Power Consumption in Agent Based Multi-Hop Wireless Networks
    Author(s): M. Udaiyanathan, A. Kaul

  4. Supply Chain Optimization using Multi-Objective Evolutionary Algorithms
    Author(s): E.G. Pinto

  5. Interplanetary Trajectory Optimization using a Genetic Algorithm
    Author(s) : A. Weeks

2003 Reports

  1. Feature Selection for Classification of Hyperspectral Remotely Sensed data using NSGA-II.
    Author(s): M. Kumar

  2. Application of Genetic Algorithms To Vehicle Suspension Design
    Author(s): H. Yu, N. Yu 

  3. Clustering of Activity Patterns Using Genetic Algorithms
    Author(s): O. Pribyl

  4. Multiobjective Genetic Algorithm for Product Design
    Author(s): J. Nanda

  5. 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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