1st Edition
Evolutionary Computation
420 Pages
by
CRC Press
424 Pages
by
CRC Press
Also available as eBook on:
Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation.
Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.
Principles of Evolutionary Computation
Genes and chromosomes
Early EC research
Basic evolutionary computation models
Other EC approaches
Structure of an evolutionary algorithm
Basic evolutionary algorithm
Genetic Algorithms
Problem representation and fitness function
Search progress
Basic elements of genetic algorithms
Canonical genetic algorithm
Schemata and building blocks
Basic Selection Schemes in Evolutionary Algorithms
Selection purposes
Fitness function
Selection pressure and takeover time
Proportional selection
Truncation
Selection Based on Scaling and Ranking Mechanisms
Scale transformation
Static scaling mechanisms
Dynamic scaling
Noisy fitness functions
Fitness remapping for minimization problems
Rank-based selection
Binary tournament
q-tournament selection
Further Selection Strategies
Classification of selection strategies
Elitist strategies
Generation gap methods
Steady-state evolutionary algorithms
Generational elitist strategies in GAs
Michalewicz selection
Boltzmann selection
Other selection methods
Genetic drift
Recombination Operators within Binary Encoding
One-point crossover
Two-point crossover
N-point crossover
Punctuated crossover
Segmented crossover
Shuffle crossover
Uniform crossover
Other crossover operators and some comparisons
Crossover probability
Mating
N-point crossover algorithm
Selection for survival or replacement
General remarks about crossover within the framework of binary encoding
Mutation and other Search Operators
Mutation with binary encoding
Strong and weak mutation operators
Non-uniform mutation
Adaptive non-uniform mutation
Self-adaptation of mutation rate
Crossover versus mutation
Inversion operator
Selection versus variation operators
Simple genetic algorithm revisited
Schema Theorem, Building Blocks and Related Topics
Elements characterizing schemata
Schema dynamics
Effect of selection on schema dynamics
Effect of recombination on schema dynamics
Combined effect of selection and recombination on schema dynamics
Effect of mutation on schema dynamics
Schema theorem
Building block
Building block hypothesis and linkage problem
Generalizations of schema theorem
Deceptive functions
Real-Valued Encoding
Real-valued vectors
Recombination operators for real-valued encoding
Mutation operators for real-valued encoding
Hybridization, Parameter Setting and Adaptation
Specialized representation and hybridization within GAs
Parameter setting and adaptive GAs
Adaptive GAs
Adaptive Representations: Messy Genetic Algorithms, Delta Coding and Diploidic Representation
Principles of messy genetic algorithms
Recombination within messy genetic operators
Mutation
Computational model and results on messy GAs
Generalizations of messy GAs
Other adaptive representation approaches
Delta coding
Diploidy and dominance
Evolution Strategies and Evolutionary Programming
Evolution strategies
(1+1) strategy
Multimembered evolution strategies
Standard mutation
Genotypes including covariance matrix. Correlated mutation
Cauchy perturbations
Evolutionary programming
Evolutionary programming using Cauchy perturbation
Population Models and Parallel Implementations
Niching methods
Fitness sharing
Crowding
Island and stepping stone models
Fine-grained and diffusion models
Coevolution
Baldwin effect
Parallel implementation of evolutionary algorithms
Genetic Programming
Early GP approaches
Program generating language
GP program structures
Initialization of tree structures
Fitness calculation
Recombination operators
Mutation
Selection
Population models
Parallel implementation
Basic GP algorithm
Learning Classifier Systems
Michigan and Pittsburg families of learning classifier systems
Michigan classifier systems
Bucket brigade algorithm
Pittsburgh classifier systems
Fuzzy classifier systems
Applications of Evolutionary Computation
General applications of evolutionary computation
Main application areas
Optimization and search applications
Choosing a decision strategy
Neural network training and design
Pattern recognition applications
Cellular automata
Evolutionary algorithms versus other heuristics
Genes and chromosomes
Early EC research
Basic evolutionary computation models
Other EC approaches
Structure of an evolutionary algorithm
Basic evolutionary algorithm
Genetic Algorithms
Problem representation and fitness function
Search progress
Basic elements of genetic algorithms
Canonical genetic algorithm
Schemata and building blocks
Basic Selection Schemes in Evolutionary Algorithms
Selection purposes
Fitness function
Selection pressure and takeover time
Proportional selection
Truncation
Selection Based on Scaling and Ranking Mechanisms
Scale transformation
Static scaling mechanisms
Dynamic scaling
Noisy fitness functions
Fitness remapping for minimization problems
Rank-based selection
Binary tournament
q-tournament selection
Further Selection Strategies
Classification of selection strategies
Elitist strategies
Generation gap methods
Steady-state evolutionary algorithms
Generational elitist strategies in GAs
Michalewicz selection
Boltzmann selection
Other selection methods
Genetic drift
Recombination Operators within Binary Encoding
One-point crossover
Two-point crossover
N-point crossover
Punctuated crossover
Segmented crossover
Shuffle crossover
Uniform crossover
Other crossover operators and some comparisons
Crossover probability
Mating
N-point crossover algorithm
Selection for survival or replacement
General remarks about crossover within the framework of binary encoding
Mutation and other Search Operators
Mutation with binary encoding
Strong and weak mutation operators
Non-uniform mutation
Adaptive non-uniform mutation
Self-adaptation of mutation rate
Crossover versus mutation
Inversion operator
Selection versus variation operators
Simple genetic algorithm revisited
Schema Theorem, Building Blocks and Related Topics
Elements characterizing schemata
Schema dynamics
Effect of selection on schema dynamics
Effect of recombination on schema dynamics
Combined effect of selection and recombination on schema dynamics
Effect of mutation on schema dynamics
Schema theorem
Building block
Building block hypothesis and linkage problem
Generalizations of schema theorem
Deceptive functions
Real-Valued Encoding
Real-valued vectors
Recombination operators for real-valued encoding
Mutation operators for real-valued encoding
Hybridization, Parameter Setting and Adaptation
Specialized representation and hybridization within GAs
Parameter setting and adaptive GAs
Adaptive GAs
Adaptive Representations: Messy Genetic Algorithms, Delta Coding and Diploidic Representation
Principles of messy genetic algorithms
Recombination within messy genetic operators
Mutation
Computational model and results on messy GAs
Generalizations of messy GAs
Other adaptive representation approaches
Delta coding
Diploidy and dominance
Evolution Strategies and Evolutionary Programming
Evolution strategies
(1+1) strategy
Multimembered evolution strategies
Standard mutation
Genotypes including covariance matrix. Correlated mutation
Cauchy perturbations
Evolutionary programming
Evolutionary programming using Cauchy perturbation
Population Models and Parallel Implementations
Niching methods
Fitness sharing
Crowding
Island and stepping stone models
Fine-grained and diffusion models
Coevolution
Baldwin effect
Parallel implementation of evolutionary algorithms
Genetic Programming
Early GP approaches
Program generating language
GP program structures
Initialization of tree structures
Fitness calculation
Recombination operators
Mutation
Selection
Population models
Parallel implementation
Basic GP algorithm
Learning Classifier Systems
Michigan and Pittsburg families of learning classifier systems
Michigan classifier systems
Bucket brigade algorithm
Pittsburgh classifier systems
Fuzzy classifier systems
Applications of Evolutionary Computation
General applications of evolutionary computation
Main application areas
Optimization and search applications
Choosing a decision strategy
Neural network training and design
Pattern recognition applications
Cellular automata
Evolutionary algorithms versus other heuristics
Biography
Beatrice Lazzerini, D. Dumitrescu, Lakhmi C. Jain. A. Dumitrescu
"…offers a presentation of the main ideas, models, and algorithms of evolutionary computation. Anyone who is inspired by the work of L. Davis or D.E. Goldberg to use evolutionary computation as a tool in hos/her daily work will find a complete overview of techniques and strategies…Anyone interested in optimization or search problems can find useful ideas, methods, and algorithms."
- Journal of Chemometrics, 2002
