Evolutionary Computation

Evolutionary Computation provides 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.

Features:

• Presents a thorough review of evolutionary computation techniques and applications
• Includes detailed coverage of encoding, selection and search operators, schemata theory, GA parameter setting, hybridization, and other genetic algorithm approaches
• Covers techniques beyond the genetic algorithms
• Details various evolution strategies
• Describes real-world applications

• 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