Genetic algorithms (GAs) are computer-based search techniques patterned after the genetic mechanisms of biological organisms that have adapted and flourished in changing, highly competitive environments for millions of years. GAs have been successfully applied to problems in a variety of studies, and their popularity continues to increase because of their effectiveness, applicability, and ease of use.
Industrial Applications of Genetic Algorithms shows how GAs have made the leap form their origins in the laboratory to the practicing engineer's toolbox. Each chapter in the book describes a project completed by a graduate student at the University of Alabama.
Table of Contents
Genetic Algorithms' Place in the Engineer's Toolbox
Image-Calibration Transformation Matrix Solution Using a Genetic Algorithm
Genetic Algorithms for H2 Controller Synthesis
Software Test Data Generation from a Genetic Algorithm
Optimization of a Porous Liner for Transpiration Cooling Using a Genetic Algorithm
Genetic Algorithms Approach to Constrained Service Provisioning
Using a Genetic Algorithm to Determine the Optimum Two-Impulse Transfer between Co-Planar, Elliptical Orbits
Optimized Non-Coplanar Orbital Transfers Using Genetic Algorithms
Data Mining Using Genetic Algorithms
Space Shuttle Main Engine Condition Monitoring via Genetic Algorithm and Radial Basis Function Neural Network
Tuning Bama Optimized Recurrent Neural Networks Using Genetic Algorithms
Gauss-Legendre Integration Using Genetic Algorithms
Using Genetic Operators to Distinguish Chaotic Behavior from Noise in a Time Series
Development of Mobile Robot Wall-Following Algorithms Using Genetic Programming
Hydrocyclone Model Using Genetic Programming
What Can I Do with a Learning Classifier System?
Genetic Algorithms for Game Playing
Simulation of an Artificial Eco-System Using Genetic Algorithms
Karr, Charles; Freeman, L. Michael