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.

Contents:

• 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

Readership: General / Popular; Enthusiasts in science, engineering and apologetics and to those interested in the information theoretic components of closely examined evolution.