Introduction to Evolutionary Informatics

Science has made great strides in modeling space, time, mass and energy. Yet little attention has been paid to the precise representation of the information ubiquitous in nature.

Introduction to Evolutionary Informatics fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits. Built on the foundation of a series of peer-reviewed papers published by the authors, the book is written at a level easily understandable to readers with knowledge of rudimentary high school math. Those seeking a quick first read or those not interested in mathematical detail can skip marked sections in the monograph and still experience the impact of this new and exciting model of nature's information.

This book is written for enthusiasts in science, engineering and mathematics interested in understanding the essential role of information in closely examined evolution theory.

• Introduction::
  • The Queen of Scientists & Engineers
  • Science and Models
• Information. What is It?:
  • Defining Information
  • Measuring Information
  • Remarks
• Design Search in Evolution and the Requirement of Intelligence:
  • Design as Search
  • Design by Computer
  • Designing a Good Pancake
  • Sources of Knowledge
  • The Curse of Dimensionality & the Need For Knowledge
  • Implicit Targets
  • Skeptic Fallibility
  • A Smörgåsbord of Search Algorithms
  • Conclusions
• Determinism in Randomness:
  • Bernoulli's Principle of Insufficient Reason
  • The Need for Noise
  • Basener's Ceiling
  • Final Comments
• Conservation of Information in Computer Search:
  • The Genesis
  • What is Conservation of Information?
  • The Astonishing Cost of Blind Search in Bits
  • Measuring Search Difficulty in Bits
  • Sources of Information in Evolutionary Search
  • Stairstep Information & Transitional Functional Viability
  • Coevolution
  • The Search for the Search
  • Conclusion
• Analysis of Some Biologically Motivated Evolutionary Models:
  • EV: A Software Model of Evolution
  • Avida: Stair Steps to Complexity Using NAND Logic
  • Metabiology
  • Conclusion: Sweeping a Dirt Floor
• Measuring Meaning: Algorithmic Specified Complexity:
  • The Meaning of Meaning
  • Conditional KCS Complexity
  • Defining Algorithmic Specified Complexity (ASC)
  • Examples of ASC
  • Meaning is in the Eye of the Beholder
• Intelligent Design & Artificial Intelligence:
  • Turing & Lovelace: One is Strong and the Other One's Dead
  • ID & the Unknowable

Robert J Marks II is Distinguished Professor of Engineering in the Department of Engineering at Baylor University, USA. Marks's professional awards include a NASA Tech Brief Award and a best paper award from the American Brachytherapy Society for prostate cancer research. He is Fellow of both IEEE and The Optical Society of America. His consulting activities include: Microsoft Corporation, DARPA, and Boeing Computer Services. He is listed as one of the "The 50 Most Influential Scientists in the World Today." By TheBestSchools.org. (2014). His contributions include: the Zhao-Atlas-Marks (ZAM) time-frequency distribution in the field of signal processing, and the Cheung–Marks theorem in Shannon sampling theory.

Marks's research has been funded by organizations such as the National Science Foundation, General Electric, Southern California Edison, the Air Force Office of Scientific Research, the Office of Naval Research, the United States Naval Research Laboratory, the Whitaker Foundation, Boeing Defense, the National Institutes of Health, The Jet Propulsion Lab, Army Research Office, and NASA. His books include Handbook of Fourier Analysis and Its Applications (Oxford University Press), Introduction to Shannon Sampling and Interpolation Theory (Springer Verlag), and Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks (MIT Press) with Russ Reed. Marks has edited/co-edited five other volumes in fields such as power engineering, neural networks, and fuzzy logic. He was instrumental in defining the discipline of computational intelligence (CI) and is a co-editor of the first book using CI in the title: Computational Intelligence: Imitating Life (IEEE Press, 1994). His authored/coauthored book chapters include nine papers reprinted in collections of classic papers. Other book chapters include contributions to Michael Arbib's The Handbook of Brain Theory and Neural Networks (MIT Press, 1996), and Michael Licona et al.'s Evidence for God (Baker Books, 2010), Marks has also authored/co-authored hundreds of peer-reviewed conference and journal papers.

William A Dembski is Senior Research Scientist at the Evolutionary Informatics Lab in McGregor, Texas; and also an entrepreneur developing educational websites and software. He holds a BA in Psychology, MS in Statistics, PhD in Philosophy, and a PhD in Mathematics (awarded in 1988 by the University of Chicago, Chicago, Illinois, USA), and an MDiv degree from Princeton Theological Seminary (1996, New Jersey, USA). Dembski's work experience includes being an Associate Research Professor with the Conceptual Foundations of Science, Baylor University, Waco, Texas, USA. He has taught at Northwestern University, Evanston, Illinois, USA; the University of Notre Dame, Notre Dame, Indiana, USA; and the University of Dallas, Irving, Texas, USA. He has done postdoctoral work in mathematics with the Massachusetts Institute of Technology, Cambridge, USA; in physics with the University of Chicago, USA; and in computer science with Princeton University, Princeton, New Jersey, USA. He is a Mathematician and Philosopher. He has held National Science Foundation graduate and postdoctoral fellowships, and has published articles in mathematics, engineering, philosophy, and theology journals and is the author/editor of more than twenty books.

Winston Ewert is currently a Software Engineer in Vancouver, Canada. He is a Senior Research Scientist at the Evolutionary Informatics Lab. Ewert holds a PhD from Baylor University, Waco, Texas, USA. He has written a number of papers relating to search, information, and complexity including studies of computer models purporting to describe Darwinian evolution and developing information theoretic models to measure specified complexity.

"An honest attempt to discuss what few people seem to realize is an important problem. Thought provoking!"

"With penetrating brilliance, and with a masterful exercise of pedagogy and wit, the authors take on Chaitin's challenge, that Darwin's theory should be subjectable to a mathematical assessment and either pass or fail. Surveying over seven decades of development in algorithmics and information theory, they make a compelling case that it fails."

"Introduction to Evolutionary Informatics is a lucid, entertaining, even witty discussion of important themes in evolutionary computation, relating them to information theory. It's far more than that, however. It is an assessment of how things might have come to be the way they are, applying an appropriate scientific skepticism to the hypothesis that random processes can explain many observed phenomena. Thus the book is appropriate for the expert and non-expert alike."

"Darwinian pretensions notwithstanding, Marks, Dembski, and Ewert demonstrate rigorously and humorously that no unintelligent process can account for the wonders of life."

"A very helpful book on this important issue of information. Information is the jewel of all science and engineering which is assumed but barely recognised in working systems. In this book Marks, Dembski and Ewert show the major principles in understanding what information is and show that it is always associated with design."

"Though somewhat difficult, Marks, Dembski and Ewert have done a masterful job of making the book accessible to the engaged and thoughtful layperson. I could not endorse this book more highly."

"This is an important and much needed step forward in making powerful concepts available at an accessible level."

"This is a fine summary of an extremely interesting body of work. It is clear, well-organized, and mathematically sophisticated without being tedious (so many books of this sort have it the other way around). It should be read with profit by biologists, computer scientists, and philosophers."

"Evolution requires the origin of new information. In this book, information experts Bob Marks, Bill Dembski, and Winston Ewert provide a comprehensive introduction to the models underlying evolution and the science of design. The authors demonstrate clearly that all evolutionary models rely implicitly on information that comes from intelligent design, and that unguided evolution cannot deliver what its promoters advertise. Though mathematically rigorous, the book is written primarily for non-mathematicians. I recommend it highly."

"Introduction to Evolutionary Informatics helps the non-expert reader grapple with a fundamental problem in science today: We cannot model information in the same way as we model matter and energy because there is no relationship between the metrics. As a result, much effort goes into attempting to explain information away. The authors show, using clear and simple illustrations, why that approach not only does not work but [that it also] impedes understanding of our universe."