基本説明
Features: Gives an overview of differing approaches to the theory of Evolutionary Algorithms; With extensive entry level tutorials written by leading experts in the field.
Full Description
During the first week of September 1999, the Second EvoNet Summer School on Theoretical Aspects of Evolutionary Computing was held at the Middelheim cam pus of the University of Antwerp, Belgium. Originally intended as a small get together of PhD students interested in the theory of evolutionary computing, the summer school grew to become a successful combination of a four-day workshop with over twenty researchers in the field and a two-day lecture series open to a wider audience. This book is based on the lectures and workshop contributions of this summer school. Its first part consists of tutorial papers which introduce the reader to a num ber of important directions in the theory of evolutionary computing. The tutorials are at graduate level andassume only a basic backgroundin mathematics and com puter science. No prior knowledge ofevolutionary computing or its theory is nec essary. The second part of the book consists of technical papers, selected from the workshop contributions. A number of them build on the material of the tutorials, exploring the theory to research level. Other technical papers may require a visit to the library.
Contents
I: 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.
