Evolutionary Optimization Algorithms.

By: Simon, DanPublisher: Somerset : John Wiley & Sons, Incorporated, 2013Copyright date: ©2013Edition: 1st edDescription: 1 online resource (776 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9781118659502Subject(s): Evolutionary computation.;Computer algorithms.;Biologically-inspired computingGenre/Form: Electronic books. Additional physical formats: Print version:: Evolutionary Optimization AlgorithmsDDC classification: 006.3 LOC classification: QA76.9.A43.S536 201Online resources: Click to View
Contents:
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.
Summary: A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear-but theoretically rigorous-understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs-including opposition-based learning, artificial fish swarms, bacterial foraging, and many others- and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
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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.

A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear-but theoretically rigorous-understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs-including opposition-based learning, artificial fish swarms, bacterial foraging, and many others- and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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