Cultural Algorithms

A thorough look at how societies can use cultural algorithms to understand human social evolution For those working in computational intelligence, developing an understanding of how cultural algorithms and social intelligence form the essential framework for the evolution of human social interaction is essential. This book, Cultural Algorithms: Tools to Model Complex Dynamic Social Systems, is the foundation of that study. It showcases how we can use cultural algorithms to organize social structures and develop socio-political systems that work. For such a vast topic, the text covers everything from the history of the development of cultural algorithms and the basic framework with which it was organized. Readers will also learn how other nature-inspired algorithms can be expressed and how to use social metrics to assess the performance of various algorithms. In addition to these topics, the book covers topics including: The CAT system including the Repast Simphony System and CAT Sample Runs How to problem solve using social networks in cultural algorithms with auctions Understanding Common Value Action to enhance Social Knowledge Distribution Systems Case studies on team formations An exploration of virtual worlds using cultural algorithms For industry professionals or new students, Cultural Algorithms provides an impactful and thorough look at both social intelligence and how human social evolution translates into the modern world.

This book covers the latest advances in Cultural Algorithms, their general framework, different variants, hybridized versions with other meta-heuristic and search techniques, and their applications. Cultural Algorithms are meta-heuristic numerical optimization techniques inspired by the bio-cultural evolutionary theory, in which both types of vertical and horizontal learning behaviors are modeled. The book includes well-briefed basics of optimization and theoretical backgrounds of Cultural Algorithms in its initial chapters and then discusses their applications in different branches of science and engineering. It provides detailed mathematical formulations and algorithmic pseudo-codes of hybridized, extended, and multi-population variants of cultural algorithms. The book will serve the research students, fellows, professors, and industry professionals to implement real-time applications of Cultural Algorithms.

The author first introduces the basic framework for cultural algorithms and he then explains the social structure of a cultural system as a mechanism for the distribution of problem-solving information throughout a population. Three different models for social organizations are presented: the homogeneous (nuclear family), heterogeneous (expanded family), and subculture (descent groups) social models. The chapters that follow compare the learning capabilities of these social organizations relative to problems of varying complexity. The book concludes with a discussion of how the results can impact our understanding of social evolution.

A thorough look at how societies can use cultural algorithms to understand human social evolution For those working in computational intelligence, developing an understanding of how cultural algorithms and social intelligence form the essential framework for the evolution of human social interaction is essential. This book, Cultural Algorithms: Tools to Model Complex Dynamic Social Systems, is the foundation of that study. It showcases how we can use cultural algorithms to organize social structures and develop socio-political systems that work. For such a vast topic, the text covers everything from the history of the development of cultural algorithms and the basic framework with which it was organized. Readers will also learn how other nature-inspired algorithms can be expressed and how to use social metrics to assess the performance of various algorithms. In addition to these topics, the book covers topics including: The CAT system including the Repast Simphony System and CAT Sample Runs How to problem solve using social networks in cultural algorithms with auctions Understanding Common Value Action to enhance Social Knowledge Distribution Systems Case studies on team formations An exploration of virtual worlds using cultural algorithms For industry professionals or new students, Cultural Algorithms provides an impactful and thorough look at both social intelligence and how human social evolution translates into the modern world.

Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author

How culture evolves through algorithms rather than knowledge inherited from ancestors. From our hunter-gatherer days, we humans evolved to be excellent throwers, chewers, and long-distance runners. We are highly social, crave Paleolithic snacks, and display some gendered difference resulting from mate selection. But we now find ourselves binge-viewing, texting while driving, and playing Minecraft. Only the collective acceleration of cultural and technological evolution explains this development. The evolutionary psychology of individuals—the drive for “food and sex”—explains some of our current habits, but our evolutionary success, Alex Bentley and Mike O'Brien explain, lies in our ability to learn cultural know-how and to teach it to the next generation. Today, we are following social media bots as much as we are learning from our ancestors. We are radically changing the way culture evolves. Bentley and O'Brien describe how the transmission of culture has become vast and instantaneous across an Internet of people and devices, after millennia of local ancestral knowledge that evolved slowly. Long-evolved cultural knowledge is aggressively discounted by online algorithms, which prioritize popularity and recency. If children are learning more from Minecraft than from tradition, this is a profound shift in cultural evolution. Bentley and O'Brien examine the broad and shallow model of cultural evolution seen today in the science of networks, prediction markets, and the explosion of digital information. They suggest that in the future, artificial intelligence could be put to work to solve the problem of information overload, learning to integrate concepts over the vast idea space of digitally stored information.

This book provides a handbook of algorithmic recipes from the fields of Metaheuristics, Biologically Inspired Computation and Computational Intelligence that have been described in a complete, consistent, and centralized manner. These standardized descriptions were carefully designed to be accessible, usable, and understandable. Most of the algorithms described in this book were originally inspired by biological and natural systems, such as the adaptive capabilities of genetic evolution and the acquired immune system, and the foraging behaviors of birds, bees, ants and bacteria. An encyclopedic algorithm reference, this book is intended for research scientists, engineers, students, and interested amateurs. Each algorithm description provides a working code example in the Ruby Programming Language.

Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficiency is measured by their ability to find acceptable results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This volume 'Nature-Inspired Algorithms for Optimisation' is a collection of the latest state-of-the-art algorithms and important studies for tackling various kinds of optimisation problems. It comprises 18 chapters, including two introductory chapters which address the fundamental issues that have made optimisation problems difficult to solve and explain the rationale for seeking inspiration from nature. The contributions stand out through their novelty and clarity of the algorithmic descriptions and analyses, and lead the way to interesting and varied new applications.