Introduction
Simulating Evolution: Basics about Genetic Algorithms
The Evolution of Evolutionary Computation
The Basics of Genetic Algorithms (GAs)
Biological Terminology
Genetic Operators
Problem Representation
GA Theory: Schemata and Building Blocks
Parallel Genetic Algorithms
The Interplay of Genetic Operators
Bibliographic Remarks
Evolving Programs: Genetic Programming
Introduction: Main Ideas and Historical Background
Chromosome Representation
Basic Steps of the Genetic Programming (GP)-Based Problem Solving Process
Typical Applications of GP
GP Schema Theories
Current GP Challenges and Research Areas
Conclusion
Bibliographic Remarks
Problems and Success Factors
What Makes GAs and GP Unique Among Intelligent Optimization Methods?
Stagnation and Premature Convergence
Preservation of Relevant Building Blocks
What Can Extended Selection Concepts Do to Avoid Premature Convergence?
Offspring Selection (OS)
The Relevant Alleles Preserving Genetic Algorithm (RAPGA)
Consequences Arising out of Offspring Selection and RAPGA
SASEGASA—More Than the Sum of All Parts
The Interplay of Distributed Search and Systematic Recovery of Essential Genetic Information
Migration Revisited
SASEGASA: A Novel and Self-Adaptive Parallel Genetic Algorithm
Interactions between Genetic Drift, Migration, and Self-Adaptive Selection Pressure
Analysis of Population Dynamics
Parent Analysis
Genetic Diversity
Characteristics of Offspring Selection and the RAPGA
Introduction
Building Block Analysis for Standard GAs
Building Block Analysis for GAs Using Offspring Selection
Building Block Analysis for the RAPGA
Combinatorial Optimization: Route Planning
The Traveling Salesman Problem
The Capacitated Vehicle Routing Problem
Evolutionary System Identification
Data-Based Modeling and System Identification
GP-Based System Identification in HeuristicLab
Local Adaption Embedded in Global Optimization
Similarity Measures for Solution Candidates
Applications of Genetic Algorithms: Combinatorial Optimization
The Traveling Salesman Problem
Capacitated Vehicle Routing
Data-Based Modeling with Genetic Programming
Time Series Analysis
Classification
Genetic Propagation
Single Population Diversity Analysis
Multi-Population Diversity Analysis
Code Bloat, Pruning, and Population Diversity
Conclusion and Outlook
Symbols and Abbreviations
References
Index