Cultural algorithms : tools to model complex dynamic social systems / Robert G. Reynolds.

Includes bibliographical references and index.

Table of Contents
List of Contributors ix

About the Companion Website xi

1 System Design Using Cultural Algorithms 1
Robert G. Reynolds

Introduction 1

The Cultural Engine 4

Outline of the Book: Cultural Learning in Dynamic Environments 6

References 10

2 The Cultural Algorithm Toolkit System 11
Thomas Palazzolo

CAT Overview 11

Downloading and Running CAT 14

The Repast Simphony System 15

Knowledge Sources 15

Fitness Functions 18

ConesWorld 19

The Logistics Function 23

CAT Sample Runs: ConesWorld 24

CAT Sample Runs: Other Problems 32

Reference 34

3 Social Learning in Cultural Algorithms with Auctions 35
Robert G. Reynolds and Leonard Kinnaird-Heether

Introduction 35

Cultural Algorithms 37

Subcultured Multi-Layered, Deep Heterogeneous Networks 40

Auction Mechanisms 42

The Cultural Engine 45

ConesWorld 47

Experimental Framework 50

Results 50

Conclusions 54

References 55

4 Using Common Value Auction in Cultural Algorithm to Enhance Robustness and Resilience of Social Knowledge Distribution Systems 57
Anas AL-Tirawi and Robert G. Reynolds

Cultural Algorithms 57

Common Value Auction 62

ConesWorld 64

Dynamic Experimental Framework 66

Results 67

Conclusions and Future Work 73

References 73

5 Optimizing AI Pipelines: A Game-Theoretic Cultural Algorithms Approach 75
Faisal Waris and Robert G. Reynolds

Introduction 75

Overview of Cultural Algorithms 77

CA Knowledge Distribution Mechanisms 78

Primer on Game Theory 80

Game- Theoretic Knowledge Distribution 81

Continuous-Action Iterated Prisoner’s Dilemma 82

Test Results: Benchmark Problem 89

Test Results: Computer Vision Pipeline 92

Conclusions 95

References 96

6 Cultural Algorithms for Social Network Analysis: Case Studies in Team Formation 98
Kalyani Selvarajah, Ziad Kobti, and Mehdi Kargar

Introduction 98

Application of Social Network 99

Forming Successful Teams 99

Formulating TFP 100

Communication Cost 101

Personnel Cost 101

Distance Cost 102

Workload Balance 102

Why Artificial Intelligence? 103

Cultural Algorithms 103

Forming Teams in Coauthorship Network 104

Individual Representation 105

Fitness Function 107

Belief Space 107

Dataset and Observations 108

Skill Frequency 108

Forming Teams in Health-care Network 108

Individual Representation 113

Fitness Function 114

Dataset and Observation 115

Summary and Conclusion 117

References 117

7 Evolving Emergent Team Strategies in Robotic Soccer using Enhanced Cultural Algorithms 119
Mostafa Z. Ali, Mohammad I. Daoud, Rami Alazrai, and Robert G. Reynolds

Introduction 119

Related Work 121

The 2D Soccer Simulation Test Bed 122

Evolution of Team Strategies via Cultural Algorithm 124

Experiments and Analysis of Results 132

Conclusion 138

References 139

8 The Use of Cultural Algorithms to Learn the Impact of Climate on Local Fishing Behavior in Cerro Azul, Peru 143
Khalid Kattan, Robert G. Reynolds, and Samuel Dustin Stanley

Introduction 143

An Overview of the Cerro Azul Fishing Dataset 143

Data Mining at the Macro, Meso, and Micro Levels 148

Cultural Algorithms and Multiobjective Optimization 149

The Artisanal Fishing Model 153

The Experimental Results 159

Statistical Validation 163

Conclusions and Future Work 166

References 167

9 CAPSO: A Parallelized Multiobjective Cultural Algorithm Particle Swarm Optimizer 169
Samuel Dustin Stanley, Khalid Kattan, and Robert G. Reynolds

Introduction 169

Multiobjective Optimization 170

Cultural Algorithms 171

CAPSO Knowledge Structures 174

Tracking Knowledge Source Progress (Other than Topographic) 176

CAPSO Algorithm Pseudocode 177

Multiple Runs 180

Comparison of Benchmark Problems 180

Overall Summary of Results 192

Other Applications 192

References 193

10 Exploring Virtual Worlds with Cultural Algorithms: Ancient Alpena–Amberley Land Bridge 195
Thomas Palazzolo, Robert G. Reynolds, and Samuel Dustin Stanley

Archaeological Challenges 195

Generalized Framework 198

The Land Bridge Hypothesis 199

Origin and Form 204

Putting Data to Work 205

Pathfinding and Planning 215

Identifying Good Locations: The Hotspot Finder 218

Cultural Algorithms 222

Cultural Algorithm Mechanisms 225

The Composition of the Belief Space 226

Future Work 227

Path Planning Strategy 227

Local Tactics 229

Detailed Locational Information 230

Extending the CA 231

Human Presence in the Virtual World 234

Increasing the Complexity 235

Updated Path-Planning Results in Unity 236

The Fully Rendered Land Bridge 237

Pathfinder Mechanisms 239

Results 245

Conclusions 254

References 255

Index 259

"'The Foundations of Social Intelligence' covers a wide range of the basic framework of cultural algorithms, from the history of its development to how other nature-inspired algorithms can be expressed. It also demonstrates how the social organizational structures that make up human socio-political systems can be modeled in terms of cultural algorithms. It explores how the learning process is expressed in thermodynamic terms as a cultural engine, while proposing several social metrics to assess their performance. Cultural algorithms are a computational framework for understanding human social evolution based upon anthropological and archaeological models of cultural evolution. The key is how we can we use cultural algorithms as a vehicle to understand why these building blocks are ubiquitous across the globe in computational terms. The performance of the basic social models are compared against each other relative to the different categories of complex systems problems"-- Provided by publisher.

About the Author
DR. ROBERT G. REYNOLDS is a Professor of Computer Science at Wayne State University and a Visiting Research Scientist at the University of Michigan's Museum of Anthropology. In addition to serving as the Computational Intelligence Representative to the IEEE USA Research and Development Committee, he has also been an Associate Editor for eight Intelligent System and IEEE journals.