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Taming the Complexity of Evolutionary Dynamics: From Microscopic Models to Schema Theory and Beyond

New
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

By: Riccardo Poli (Author), Christopher R Stephens (Author)

Springer-Verlag

Hardback | Jun 2016 | #201396 | ISBN-13: 9783642173608
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NHBS Price: £64.99 $82/€77 approx

About this book

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.


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


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