Logo - springer
Slogan - springer

Computer Science - Theoretical Computer Science | Taming the Complexity of Evolutionary Dynamics - From Microscopic Models to Schema Theory and

Taming the Complexity of Evolutionary Dynamics

From Microscopic Models to Schema Theory and Beyond

Stephens, Christopher R., Poli, Riccardo

2014, X, 480 p.

Available Formats:

Springer eBooks may be purchased by end-customers only and are sold without copy protection (DRM free). Instead, all eBooks include personalized watermarks. This means you can read the Springer eBooks across numerous devices such as Laptops, eReaders, and tablets.

You can pay for Springer eBooks with Visa, Mastercard, American Express or Paypal.

After the purchase you can directly download the eBook file or read it online in our Springer eBook Reader. Furthermore your eBook will be stored in your MySpringer account. So you can always re-download your eBooks.


ISBN 978-3-642-17361-5

digitally watermarked, no DRM

The eBook version of this title will be available soon

learn more about Springer eBooks

add to marked items


Hardcover version

You can pay for Springer Books with Visa, Mastercard, American Express or Paypal.

Standard shipping is free of charge for individual customers.

approx. $109.00

(net) price for USA

ISBN 978-3-642-17360-8

free shipping for individuals worldwide

Due: July 10, 2016

add to marked items

  • Details the most significant, comprehensive theory of artificial evolutionary systems to emerge in the last 10 years
  • A key resource for theoreticians in computer science, artificial intelligence, engineering, biology and physics
  • Written by the top theoreticians in the field of evolutionary computation

The study of complex adaptive systems is among the key modern tasks in science. Such systems show radically different behaviours at different scales and in different environments, and mathematical modelling of such emergent behaviour is very difficult, even at the conceptual level. We require a new methodology to study and understand complex, emergent macroscopic phenomena. Coarse graining, a technique that originated in statistical physics, involves taking a system with many microscopic degrees of freedom and finding an appropriate subset of collective variables that offer a compact, computationally feasible description of the system, in terms of which the dynamics looks “natural”.


The authors explain the basics of natural and artificial evolutionary dynamics, and offer detailed treatments of the related models of search spaces, population spaces, state spaces, crossover, mutation and selection. The rest of the book is concerned with the mathematical modelling of these aspects of evolutionary dynamics using the coarse graining technique, and with analysis of the subsequent models.


This book is a significant contribution to the theory of artificial evolutionary systems, and will be key reading for theoreticians in computer science, artificial intelligence and engineering. While the insights into how complexity can be tamed will be valuable reading for biologists and physicists engaged with the theory of natural evolutionary systems.


Content Level » Research

Keywords » App. A - App. B - Artificial Evolutionary Dynamics - Building Blocks - Coarse Graining - Conclusions and Challenges - Evolutionary Algorithms - Evolutionary Dynamics and Signal Processing - Fitness Landscapes - Genetic Dynamics - Index - Introduction - Lessons for Biology - Mathematical Preliminaries - Microscopic Models - Models of Crossover - Models of Mutation - Models of Search Spaces - Models of Selection - Natural Evolutionary Dynamics - Population Spaces and State Spaces - Recombination and Mutation - References - Search Algorithms with Fixed-Length Representations - Search Biases

Related subjects » Complexity - Computational Intelligence and Complexity - Theoretical Computer Science

Table of contents 

Introduction.- Natural Evolutionary Dynamics.- Artificial Evolutionary Dynamics.- Models of Search Spaces, Population Spaces and State Spaces.- Models of Crossover.- Models of Mutation.- Models of Selection.- Search Algorithms with Fixed-Length Representations.- Microscopic Models.- Coarse Graining.- Genetic Dynamics.- Building Blocks.- Recombination and Mutation.- Evolutionary Algorithms.- Search Biases.- Evolutionary Dynamics and Signal Processing.- Lessons for Biology.- Conclusions and Challenges.- App. A Fitness Landscapes.- App. B Mathematical Preliminaries.- References.- Index.

Popular Content within this publication 



Services for this book

New Book Alert

Get alerted on new Springer publications in the subject area of Theory of Computation.