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Evolutionary Computation A Unified Approach

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ISBN-10: 0262041944

ISBN-13: 9780262041942

Edition: 2006

Authors: Kenneth A. de Jong

List price: $61.00
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This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.
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Book details

List price: $61.00
Copyright year: 2006
Publisher: MIT Press
Publication date: 2/3/2006
Binding: Hardcover
Pages: 268
Size: 7.25" wide x 9.25" long x 0.50" tall
Weight: 1.298

Benn Steil is Andr� Meyer Senior Fellow and Director of International Economics at the Council on Foreign Relations and Editor of the journal International Finance.

Basic Evolutionary Processes
EV: A Simple Evolutionary System
EV on a Simple Fitness Landscape
EV on a More Complex Fitness Landscape
Evolutionary Systems as Problem Solvers
A Historical Perspective
Early Algorithmic Views
The Catalytic 1960s
The Explorative 1970s
Evolutionary Programming
Evolution Strategies
Genetic Algorithms
The Exploitative 1980s
Optimization Applications
Other EA Applications
The Unifying 1990s
The Twenty-first Century: Mature Expansion
Canonical Evolutionary Algorithms
Evolutionary Programming
Evolution Strategies
Genetic Algorithms
Multi-parent Reproduction
Universal Genetic Codes
A Unified View of Simple EAs
A Common Framework
Population Size
Parent Population Size m
Offspring Population Size n
Choosing Selection Mechanisms
Survival Selection: A Special Case
Selection Summary
Reproductive Mechanisms
Crossover or Mutation?
Representation Issues
Choosing Effective Reproductive Mechanisms
Evolutionary Algorithms as Problem Solvers
Simple EAs as Parallel Adaptive Search
Fixed-Length Linear Objects
Nonlinear Objects
Variable-Length Objects
Nonlinear, Variable-Length Objects
Reproductive Operators
Objective Fitness Evaluation
Population Sizes and Dynamics
Convergence and Stopping Criteria
Returning an Answer
EA-based Optimization
Fitness Scaling
Convergence and Elitism
Parameter Optimization
Phenotypic Representations and Operators
Genotypic Representations and Operators
Choosing Representations and Operators
Real-Valued Parameter Optimization
Integer-Valued Parameter Optimization
Symbolic Parameter Optimization
Non-homogeneous Parameter Optimization
Constrained Optimization
Data Structure Optimization
Variable-Length Data Structures
Multi-objective Optimization
EA-Based Search
EA-Based Machine Learning
EA-Based Automated Programming
Representing Programs
Evaluating Programs
EA-Based Adaptation
Evolutionary Computation Theory
Analyzing EA Dynamics
Selection-Only Models
Non-overlapping-Generation Models
Uniform (Neutral) Selection
Fitness-Biased Selection
Non-overlapping-Generation Models with n [not equal] m
Overlapping-Generation Models
Uniform (Neutral) Selection
Fitness-Biased Selection
Selection in Standard EAs
Reducing Selection Sampling Variance
Selection Summary
Reproduction-Only Models
Non-overlapping-Generation Models
Reproduction for Fixed-Length Discrete Linear Genomes
Reproduction for Other Genome Types
Overlapping-Generation Models
Reproduction Summary
Selection and Reproduction Interactions
Evolvability and Price's Theorem
Selection and Discrete Recombination
Discrete Recombination from a Schema Perspective
Crossover-Induced Diversity
Crossover-Induced Fitness Improvements
Selection and Other Recombination Operators
Selection and Mutation
Mutation from a Schema Perspective
Mutation-Induced Diversity
Mutation-Induced Fitness Improvements
Selection and Other Mutation Operators
Selection and Multiple Reproductive Operators
Selection, Reproduction, and Population Size
Non-overlapping-Generation Models
Overlapping-Generation Models
Capturing Important Application Features
Defining Effective Reproduction Operators
Effective Mutation Operators
Effective Recombination Operators
Landscape Analysis
Models of Canonical EAs
Infinite Population Models for Simple GAs
Expected Value Models of Simple GAs
GA Schema Theory
Markov Models
Markov Models of Finite Population EAs
Markov Models of Simple GAs
Statistical Mechanics Models
Application-Oriented Theories
Optimization-Oriented Theories
Convergence and Rates of Convergence
ESs and Real-Valued Parameter Optimization Problems
Simple EAs and Discrete Optimization Problems
Optimizing with Genetic Algorithms
Advanced EC Topics
Self-adapting EAs
Adaptation at EA Design Time
Adaptation over Multiple EA Runs
Adaptation during an EA Run
Dynamic Landscapes
Standard EAs on Dynamic Landscapes
Modified EAs for Dynamic Landscapes
Categorizing Dynamic Landscapes
The Importance of the Rate of Change
The Importance of Diversity
Exploiting Parallelism
Coarse-Grained Parallel EAs
Fine-Grained Models
Evolving Executable Objects
Representation of Behaviors
Multi-objective EAs
Hybrid EAs
Biologically Inspired Extensions
Non-random Mating and Speciation
Coevolutionary Systems
CoEC Architectures
CoEC Dynamics
Generative Representations and Morphogenesis
Inclusion of Lamarckian Properties
Agent-Oriented Models
The Road Ahead
Modeling General Evolutionary Systems
More Unification
Source Code Overview
EC1: A Very Simple EC System
EC1 Code Structure
EC1 Parameters
EC2: A More Interesting EC System
EC3: A More Flexible EC System
EC4: An EC Research System