Introduction to Evolutionary Computing

ISBN-10: 3540401849
ISBN-13: 9783540401841
Edition: 2003
List price: $49.95
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Description: Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a  More...

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Book details

List price: $49.95
Copyright year: 2003
Publisher: Springer
Publication date: 8/6/2007
Binding: Hardcover
Pages: 300
Size: 6.25" wide x 9.25" long x 1.00" tall
Weight: 1.474
Language: English

Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading-edge scientific research. This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied. Last, but not least, this book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

Introduction
Aims of this Chapter
The Main Evolutionary Computing Metaphor
Brief History
The Inspiration from Biology
Darwinian Evolution
Genetics
Evolutionary Computing: Why?
Exercises
Recommended Reading for this Chapter
What is an Evolutionary Algorithm?
Aims of this Chapter
What is an Evolutionary Algorithm?
Components of Evolutionary Algorithms
Representation (Definition of Individuals)
Evaluation Function (Fitness Function)
Population
Parent Selection Mechanism
Variation Operators
Survivor Selection Mechanism (Replacement)
Initialisation
Termination Condition
Example Applications
The Eight-Queens Problem
The Knapsack Problem
Working of an Evolutionary Algorithm
Evolutionary Computing and Global Optimisation
Exercises
Recommended Reading for this Chapter
Genetic Algorithms
Aims of this Chapter
Introductory Example
Representation of Individuals
Binary Representations
Integer Representations
Real-Valued or Floating-Point Representation
Permutation Representations
Mutation
Mutation for Binary Representations
Mutation Operators for Integer Representations
Mutation Operators for Floating-Point Representations
Mutation Operators for Permutation Representations
Recombination
Recombination Operators for Binary Representations
Recombination Operators for Integer Representations
Recombination Operators for Floating-Point Representations
Recombination Operators for Permutation Representations
Multiparent Recombination
Population Models
Parent Selection
Fitness Proportional Selection
Ranking Selection
Implementing Selection Probabilities
Tournament Selection
Survivor Selection
Age-Based Replacement
Fitness-Based Replacement
Example Application: Solving a Job Shop Scheduling Problem
Exercises
Recommended Reading for this Chapter
Evolution Strategies
Aims of this Chapter
Introductory Example
Representation
Mutation
Uncorrelated Mutation with One Step Size
Uncorrelated Mutation with n Step Sizes
Correlated Mutations
Recombination
Parent Selection
Survivor Selection
Self-Adaptation
Example Applications
The Ackley Function
Subjective Evolution of Colour Mixes
Exercises
Recommended Reading for this Chapter
Evolutionary Programming
Aims of this Chapter
Introductory Example
Representation
Mutation
Recombination
Parent Selection
Survivor Selection
Example Application
The Ackley Function
Evolving Checkers Players
Exercises
Recommended Reading for this Chapter
Genetic Programming
Aims of this Chapter
Introductory Example
Representation
Mutation
Recombination
Parent Selection
Survivor Selection
Initialisation
Bloat in Genetic Programming
Problems Involving ""Physical"" Environments
Example Application: Symbolic Regression
Exercises
Recommended Reading for this Chapter
Learning Classifier Systems
Aims of this Chapter
Introductory Example
General Background
ZCS: A ""Zeroth-Level"" Classifier System
XCS
Motivation
Description
Extensions
Example Applications
Modelling Financial Market Traders
A Multistep Problem
Exercises
Recommended Reading for this Chapter
Parameter Control in Evolutionary Algorithms
Aims of this Chapter
Introduction
Examples of Changing Parameters
Changing the Mutation Step Size
Changing the Penalty Coefficients
Summary
Classification of Control Techniques
What is Changed?
How are Changes Made?
What Evidence Informs the Change?
What is the Scope of the Change?
Summary
Examples of Varying EA Parameters
Representation
Evaluation function
Mutation
Crossover
Selection
Population
Varying Several Parameters Simultaneously
Discussion
Exercises
Recommended Reading for this Chapter
Multimodal Problems and Spatial Distribution
Aims of this Chapter
Introduction: Multimodal Problems and the Need for Diversity
Multimodal Problems
Genetic Drift
Biological Motivations and Algorithmic Approaches
Algorithmic Versus Genetic Versus Solution Space
Summary
Implicit Measures
Multiple Populations in Tandem: Island Model EAs
Spatial Distribution Within One Population: Diffusion Model EAs
Automatic Speciation Using Mating Restrictions
Explicit Diversity Maintenance
Fitness Sharing
Crowding
Multiobjective Evolutionary Algorithms
Multiobjective Optimisation Problems
Dominance and Pareto Optimality
EA Approaches to Multiobjective Optimisation
Example Application: Distributed Coevolution of Job Shop Schedules
Exercises
Recommended Reading for this Chapter
Hybridisation with Other Techniques: Memetic Algorithms
Aims of this Chapter
Motivation for Hybridising EAs
A Brief Introduction to Local Search
Lamarckianism and the Baldwin Effect
Structure of a Memetic Algorithm
Heuristic or Intelligent Initialisation
Hybridisation Within Variation Operators: Intelligent Crossover and Mutation
Local Search Acting on the Output from Variation Operators
Hybridisation During Genotype to Phenotype Mapping
Design Issues for Memetic Algorithms
Preservation of Diversity
Choice of Operators
Use of Knowledge
Example Application: Multistage Memetic Timetabling
Exercises
Recommended Reading for this Chapter
Theory
Aims of this Chapter
Competing Hyperplanes in Binary Spaces: the Schema Theorem
What is a Schema?
Holland's Formulation for the SGA
Schema-Based Analysis of Variation Operators
Walsh Analysis and Deception
Criticisms and Recent Extensions of the Schema Theorem
Gene Linkage: Identifying and Recombining Building Blocks
Dynamical Systems
Markov Chain Analysis
Statistical Mechanics Approaches
Reductionist Approaches
Analysing EAs in Continuous Search Spaces
No Free Lunch Theorems
Exercises
Recommended Reading for this Chapter
Constraint Handling
Aims of this Chapter
Constrained Problems
Free Optimisation Problems
Constraint Satisfaction Problems
Constrained Optimisation Problems
Two Main Types of Constraint Handling
Ways to Handle Constraints in EAs
Penalty Functions
Repair Functions
Restricting Search to the Feasible Region
Decoder Functions
Application Example: Graph Three-Colouring
Indirect Approach
Mixed Mapping - Direct Approach
Exercises
Recommended Reading for this Chapter
Special Forms of Evolution
Aims of this Chapter
Coevolution
Cooperative coevolution
Competitive coevolution
Example Application: Coevolutionary Constraint lSatisfaction
Interactive Evolution
Optimisation, Design, Exploration
Interactive Evolutionary Design and Art
Example Application: The Mondriaan Evolver
Nonstationary Function Optimisation
Algorithmic Approaches
Selection and Replacement Policies
Example Application: Time-Varying Knapsack Problem
Exercises
Recommended Reading for this Chapter
Working with Evolutionary Algorithms
Aims of this Chapter
What Do You Want an EA to Do?
Performance Measures
Different Performance Measures
Peak versus Average Performance
Test Problems for Experimental Comparisons
Using Predefined Problem Instances
Using Problem Instance Generators
Using Real-World Problems
Example Applications
Bad Practice
Better Practice
Exercises
Recommended Reading for this Chapter
Summary
What Is in This Book?
What Is Not in This Book?
Gray Coding
Test Functions
Unimodal Functions
OneMax
The Sphere Model
Royal Road Function
Multimodal Functions
Ackley Function
Schwefel's function
Generalized Rastrigin's function
Deb's Deceptive 4-Bit Function
Randomised Test Function Generators
Kauffman's NK Landscapes
NKC Landscapes
Random L-SAT
References
Index

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