Kallel L., Naudts B., Rogers A. (eds.).Theoretical Aspects of Evolutionary Computing

Springer, 2001. — 495.During the first week of September 1999, the Second EvoNet Summer School on Theoretical Aspects of Evolutionary Computing was held at the Middelheim campus 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 number of important directions in the theory of evolutionary computing. The tutorials are at graduate level and assume only a basic background in mathematics and computer science. No prior knowledge of evolutionary computing or its theory is necessary. 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.
The theory of evolution is at the crossroads of mathematics, computer science, statistics, and the theoretical sides of physics , chemistry, and biology. There is often too little interaction between these disciplines. One of the summer school's goals was to get researchers from many disciplines together. As a result, this book contains papers from researchers with a background in complexity theory, neural networks, probability theory, population genetics, statistical physics, and mathematics. Only a fraction of the authors grew up in the evolutionary computing community itself. This is reflected in the book, which presents a rich variety of approaches to the study of the behavior of evolutionary algorithms.Part I: Tutorials
Introduction 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
Part 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