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OverviewThis book is the first in the field to provide extensive, entry level tutorials to the theory of Evolutionary Computing, covering the main approaches to understanding the dynamics of Evolutionary Algorithms. It combines this with recent, previously unpublished research papers based on the material of the tutorials. The outcome is a book which is self-contained to a large degree, attractive both to graduate students and researchers from other fields who want to get acquainted with the theory of Evolutionary Computing, and to active researchers in the field who can use this book as a reference and a source of recent results. Full Product DetailsAuthor: Leila Kallel , Bart Naudts , Alex RogersPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Edition: Softcover reprint of hardcover 1st ed. 2001 Dimensions: Width: 15.50cm , Height: 2.60cm , Length: 23.50cm Weight: 0.770kg ISBN: 9783642086762ISBN 10: 3642086764 Pages: 499 Publication Date: 07 December 2010 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsI: Tutorials.- to Evolutionary Computing in Design Search and Optimisation.- Evolutionary Algorithms and Constraint Satisfaction: Definitions, Survey, Methodology, and Research Directions.- The Dynamical Systems Model of the Simple Genetic Algorithm.- Modelling Genetic Algorithm Dynamics.- Statistical Mechanics Theory of Genetic Algorithms.- Theory of Evolution Strategies — A Tutorial.- Evolutionary Algorithms: From Recombination to Search Distributions.- Properties of Fitness Functions and Search Landscapes.- II: Technical Papers.- A Solvable Model of a Hard Optimisation Problem.- Bimodal Performance Profile of Evolutionary Search and the Effects of Crossover.- Evolution Strategies in Noisy Environments — A Survey of Existing Work.- Cyclic Attractors and Quasispecies Adaptability.- Genetic Algorithms in Time-Dependent Environments.- Statistical Machine Learning and Combinatorial Optimization.- Multi-Parent Scanning Crossover and Genetic Drift.- Harmonic Recombination for Evolutionary Computation.- How to Detect all Maxima of a Function.- On Classifications of Fitness Functions.- Genetic Search on Highly Symmetric Solution Spaces: Preliminary Results.- Structure Optimization and Isomorphisms.- Detecting Spin-Flip Symmetry in Optimization Problems.- Asymptotic Results for Genetic Algorithms with Applications to Nonlinear Estimation.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |