http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
A Practical Approach to Global Optimization
Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
Global optimization -- The Motivation for Differential Evolution -- Critical values for the control parameters of differential evolution algorithms -- The Differential Evolution Algorithm -- Fast Evolution Strategies -- Benchmarking Differential Evolution -- On the usage of differential evolution for function optimization -- Problem Domains -- Shape design and optimization by genetic algorithm -- Architectural Aspects and Computing Environments -- Computer Code -- Computer Code -- Applications -- Genetic Algorithms and Related Techniques for Optimizing Si-H Clusters: A Merit Analysis for Differential Evolution -- Non-Imaging Optical Design Using Differential Evolution -- Optimization of an Industrial Compressor Supply System -- Minimal Representation Multi-Sensor Fusion Using Differential Evolution -- Determination of the Earthquake Hypocenter: A Challenge for the Differential Evolution Algorithm -- Parallel Differential Evolution: Application to 3-D Medical Image Registration -- Design of Efficient Erasure Codes with Differential Evolution -- FIWIZ — A Versatile Program for the Design of Digital Filters Using Differential Evolution -- Optimization of Radial Active Magnetic Bearings by Using Differential Evolution and the Finite Element Method -- Application of Differential Evolution to the Analysis of X-Ray Reflectivity Data -- Inverse Fractal Problem -- Active Compensation in RF-Driven Plasmas by Means of Differential Evolution.
by Kenneth V. Price, Rainer M. Storn, Jouni A. Lampinen.
Software Engineering/Programming and Operating Systems
Algorithm Analysis and Problem Complexity
Artificial Intelligence (incl. Robotics)