Cover -- Title Page -- Copyright Page -- SHORT TABLE OF CONTENTS -- DETAILED TABLE OF CONTENTS -- Acknowledgments -- Acronyms -- List of Algorithms -- PART I INTRODUCTION TO EVOLUTIONARY OPTIMIZATION -- 1 Introduction -- 1.1 Terminology -- 1.2 Why Another Book on Evolutionary Algorithms? -- 1.3 Prerequisites -- 1.4 Homework Problems -- 1.5 Notation -- 1.6 Outline of the Book -- 1.7 A Course Based on This Book -- 2 Optimization -- 2.1 Unconstrained Optimization -- 2.2 Constrained Optimization -- 2.3 Multi-Objective Optimization -- 2.4 Multimodal Optimization -- 2.5 Combinatorial Optimization -- 2.6 Hill Climbing -- 2.6.1 Biased Optimization Algorithms -- 2.6.2 The Importance of Monte Carlo Simulations -- 2.7 Intelligence -- 2.7.1 Adaptation -- 2.7.2 Randomness -- 2.7.3 Communication -- 2.7.4 Feedback -- 2.7.5 Exploration and Exploitation -- 2.8 Conclusion -- Problems -- PART II CLASSIC EVOLUTIONARY ALGORITHMS -- 3 Genetic Algorithms -- 3.1 The History of Genetics -- 3.1.1 Charles Darwin -- 3.1.2 Gregor Mendel -- 3.2 The Science of Genetics -- 3.3 The History of Genetic Algorithms -- 3.4 A Simple Binary Genetic Algorithm -- 3.4.1 A Genetic Algorithm for Robot Design -- 3.4.2 Selection and Crossover -- 3.4.3 Mutation -- 3.4.4 GA Summary -- 3.4.5 GA Tuning Parameters and Examples -- 3.5 A Simple Continuous Genetic Algorithm -- 3.6 Conclusion -- Problems -- 4 Mathematical Models of Genetic Algorithms -- 4.1 Schema Theory -- 4.2 Markov Chains -- 4.3 Markov Model Notation for Evolutionary Algorithms -- 4.4 Markov Models of Genetic Algorithms -- 4.4.1 Selection -- 4.4.2 Mutation -- 4.4.3 Crossover -- 4.5 Dynamic System Models of Genetic Algorithms -- 4.5.1 Selection -- 4.5.2 Mutation -- 4.5.3 Crossover -- 4.6 Conclusion -- Problems -- 5 Evolutionary Programming -- 5.1 Continuous Evolutionary Programming -- 5.2 Finite State Machine Optimization.

5.3 Discrete Evolutionary Programming -- 5.4 The Prisoner's Dilemma -- 5.5 The Artificial Ant Problem -- 5.6 Conclusion -- Problems -- 6 Evolution Strategies -- 6.1 The (1+1) Evolution Strategy -- 6.2 The 1/5 Rule: A Derivation -- 6.3 The (μ+l) Evolution Strategy -- 6.4 (μ + λ) and (μ, λ) Evolution Strategies -- 6.5 Self-Adaptive Evolution Strategies -- 6.6 Conclusion -- Problems -- 7 Genetic Programming -- 7.1 Lisp: The Language of Genetic Programming -- 7.2 The Fundamentals of Genetic Programming -- 7.2.1 Fitness Measure -- 7.2.2 Termination Criteria -- 7.2.3 Terminal Set -- 7.2.4 Function Set -- 7.2.5 Initialization -- 7.2.6 Genetic Programming Parameters -- 7.3 Genetic Programming for Minimum Time Control -- 7.4 Genetic Programming Bloat -- 7.5 Evolving Entities other than Computer Programs -- 7.6 Mathematical Analysis of Genetic Programming -- 7.6.1 Definitions and Notation -- 7.6.2 Selection and Crossover -- 7.6.3 Mutation and Final Results -- 7.7 Conclusion -- Problems -- 8 Evolutionary Algorithm Variations -- 8.1 Initialization -- 8.2 Convergence Criteria -- 8.3 Problem Representation Using Gray Coding -- 8.4 Elitism -- 8.5 Steady-State and Generational Algorithms -- 8.6 Population Diversity -- 8.6.1 Duplicate Individuals -- 8.6.2 Niche-Based and Species-Based Recombination -- 8.6.3 Niching -- 8.7 Selection Options -- 8.7.1 Stochastic Universal Sampling -- 8.7.2 Over-Selection -- 8.7.3 Sigma Scaling -- 8.7.4 Rank-Based Selection -- 8.7.5 Linear Ranking -- 8.7.6 Tournament Selection -- 8.7.7 Stud Evolutionary Algorithms -- 8.8 Recombination -- 8.8.1 Single-Point Crossover (Binary or Continuous EAs) -- 8.8.2 Multiple-Point Crossover (Binary or Continuous EAs) -- 8.8.3 Segmented Crossover (Binary or Continuous EAs) -- 8.8.4 Uniform Crossover (Binary or Continuous EAs) -- 8.8.5 Multi-Parent Crossover (Binary or Continuous EAs).

8.8.6 Global Uniform Crossover (Binary or Continuous EAs) -- 8.8.7 Shuffle Crossover (Binary or Continuous EAs) -- 8.8.8 Flat Crossover and Arithmetic Crossover (Continuous EAs) -- 8.8.9 Blended Crossover (Continuous EAs) -- 8.8.10 Linear Crossover (Continuous EAs) -- 8.8.11 Simulated Binary Crossover (Continuous EAs) -- 8.8.12 Summary -- 8.9 Mutation -- 8.9.1 Uniform Mutation Centered at xi(k) -- 8.9.2 Uniform Mutation Centered at the Middle of the Search Domain -- 8.9.3 Gaussian Mutation Centered at xi(k) -- 8.9.4 Gaussian Mutation Centered at the Middle of the Search Domain -- 8.10 Conclusion -- Problems -- PART III MORE RECENT EVOLUTIONARY ALGORITHMS -- 9 Simulated Annealing -- 9.1 Annealing in Nature -- 9.2 A Simple Simulated Annealing Algorithm -- 9.3 Cooling Schedules -- 9.3.1 Linear Cooling -- 9.3.2 Exponential Cooling -- 9.3.3 Inverse Cooling -- 9.3.4 Logarithmic Cooling -- 9.3.5 Inverse Linear Cooling -- 9.3.6 Dimension-Dependent Cooling -- 9.4 Implementation Issues -- 9.4.1 Candidate Solution Generation -- 9.4.2 Reinitialization -- 9.4.3 Keeping Track of the Best Candidate Solution -- 9.5 Conclusion -- Problems -- 10 Ant Colony Optimization -- 10.1 Pheromone Models -- 10.2 Ant System -- 10.3 Continuous Optimization -- 10.4 Other Ant Systems -- 10.4.1 Max-Min Ant System -- 10.4.2 Ant Colony System -- 10.4.3 Even More Ant Systems -- 10.5 Theoretical Results -- 10.6 Conclusion -- Problems -- 11 Particle Swarm Optimization -- 11.1 A Basic Particle Swarm Optimization Algorithm -- 11.2 Velocity Limiting -- 11.3 Inertia Weighting and Constriction Coefficients -- 11.3.1 Inertia Weighting -- 11.3.2 The Constriction Coefficient -- 11.3.3 PSO Stability -- 11.4 Global Velocity Updates -- 11.5 The Fully Informed Particle Swarm -- 11.6 Learning from Mistakes -- 11.7 Conclusion -- Problems -- 12 Differential Evolution.

12.1 A Basic Differential Evolution Algorithm -- 12.2 Differential Evolution Variations -- 12.2.1 Trial Vectors -- 12.2.2 Mutant Vectors -- 12.2.3 Scale Factor Adjustment -- 12.3 Discrete Optimization -- 12.3.1 Mixed-Integer Differential Evolution -- 12.3.2 Discrete Differential Evolution -- 12.4 Differential Evolution and Genetic Algorithms -- 12.5 Conclusion -- Problems -- 13 Estimation of Distribution Algorithms -- 13.1 Estimation of Distribution Algorithms: Basic Concepts -- 13.1.1 A Simple Estimation of Distribution Algorithm -- 13.1.2 Computations of Statistics -- 13.2 First-Order Estimation of Distribution Algorithms -- 13.2.1 The Univariate Marginal Distribution Algorithm (UMDA) -- 13.2.2 The Compact Genetic Algorithm (cGA) -- 13.2.3 Population Based Incremental Learning (PBIL) -- 13.3 Second-Order Estimation of Distribution Algorithms -- 13.3.1 Mutual Information Maximization for Input Clustering (MIMIC) -- 13.3.2 Combining Optimizers with Mutual Information Trees (COMIT) -- 13.3.3 The Bivariate Marginal Distribution Algorithm (BMDA) -- 13.4 Multivariate Estimation of Distribution Algorithms -- 13.4.1 The Extended Compact Genetic Algorithm (ECGA) -- 13.4.2 Other Multivariate Estimation of Distribution Algorithms -- 13.5 Continuous Estimation of Distribution Algorithms -- 13.5.1 The Continuous Univariate Marginal Distribution Algorithm -- 13.5.2 Continuous Population Based Incremental Learning -- 13.6 Conclusion -- Problems -- 14 Biogeography-Based Optimization -- 14.1 Biogeography -- 14.2 Biogeography is an Optimization Process -- 14.3 Biogeography-Based Optimization -- 14.4 BBO Extensions -- 14.4.1 Migration Curves -- 14.4.2 Blended Migration -- 14.4.3 Other Approaches to BBO -- 14.4.4 BBO and Genetic Algorithms -- 14.5 Conclusion -- Problems -- 15 Cultural Algorithms -- 15.1 Cooperation and Competition.

15.2 Belief Spaces in Cultural Algorithms -- 15.3 Cultural Evolutionary Programming -- 15.4 The Adaptive Culture Model -- 15.5 Conclusion -- Problems -- 16 Opposition-Based Learning -- 16.1 Opposition Definitions and Concepts -- 16.1.1 Reflected Opposites and Modulo Opposites -- 16.1.2 Partial Opposites -- 16.1.3 Type 1 Opposites and Type 2 Opposites -- 16.1.4 Quasi Opposites and Super Opposites -- 16.2 Opposition-Based Evolutionary Algorithms -- 16.3 Opposition Probabilities -- 16.4 Jumping Ratio -- 16.5 Oppositional Combinatorial Optimization -- 16.6 Dual Learning -- 16.7 Conclusion -- Problems -- 17 Other Evolutionary Algorithms -- 17.1 Tabu Search -- 17.2 Artificial Fish Swarm Algorithm -- 17.2.1 Random Behavior -- 17.2.2 Chasing Behavior -- 17.2.3 Swarming Behavior -- 17.2.4 Searching Behavior -- 17.2.5 Leaping Behavior -- 17.2.6 A Summary of the Artificial Fish Swarm Algorithm -- 17.3 Group Search Optimizer -- 17.4 Shuffled Frog Leaping Algorithm -- 17.5 The Firefly Algorithm -- 17.6 Bacterial Foraging Optimization -- 17.7 Artificial Bee Colony Algorithm -- 17.8 Gravitational Search Algorithm -- 17.9 Harmony Search -- 17.10 Teaching-Learning-Based Optimization -- 17.11 Conclusion -- Problems -- PART IV SPECIAL TYPES OF OPTIMIZATION PROBLEMS -- 18 Combinatorial Optimization -- 18.1 The Traveling Salesman Problem -- 18.2 TSP Initialization -- 18.2.1 Nearest-Neighbor Initialization -- 18.2.2 Shortest-Edge Initialization -- 18.2.3 Insertion Initialization -- 18.2.4 Stochastic Initialization -- 18.3 TSP Representations and Crossover -- 18.3.1 Path Representation -- 18.3.2 Adjacency Representation -- 18.3.3 Ordinal Representation -- 18.3.4 Matrix Representation -- 18.4 TSP Mutation -- 18.4.1 Inversion -- 18.4.2 Insertion -- 18.4.3 Displacement -- 18.4.4 Reciprocal Exchange -- 18.5 An Evolutionary Algorithm for the Traveling Salesman Problem.

18.6 The Graph Coloring Problem.