http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Genetic Algorithms applications to optimization and system identification
viii, 38 leaves : illustrations ; 28 cm.
Genetic Algorithms (GA) are very different from the traditional optimization techniques. GA is a new generation of artificial intelligence and its principles mimic the behavior of the biologic genes in the natural world. Its execution is simple and it can determine solutions in a very short time. According to these characteristics, GA is a very powerful method for optimal design and system identification. In this thesis, we will apply GA to two main topics. Chapter II is about optimal designing of journal bearings with the objective of minimizing energy dissipated through the bearings and Chapters III and IV are about identifying the unknown parameters of stiffness-damping systems and rotordynamic systems.
"Major subject: Mechanical Engineering".
Includes bibliographical references: pages 35-38.