5.9. Conclusion
Notes
6. Analysis of Some Biologically Motivated Evolutionary Models
6.1. EV: A Software Model of Evolution
6.1.1. EV structure
6.1.2. EV vivisection
6.1.3. Information sources resident in EV
6.1.4. The search
6.1.4.1. Search using the number cruncher
6.1.4.2. Evolutionary search
6.1.4.3. EV and stochastic hill climbing
6.1.4.4. Mutation rate
6.1.5. EV ware
6.1.6. The diagnosis
6.2. Avida: Stair Steps to Complexity Using NAND Logic
6.2.1. Kitzmiller et al. versus Dover area school district
6.2.2. Boolean logic
6.2.3. NAND logic
6.2.3.1. Logic synthesis using NAND gates
6.2.4. The Avida organism and its health
6.2.5. Information analysis of Avida
6.2.5.1. Performance
6.2.5.1.1. The evolutionary approach
6.2.5.1.2. The ratchet approach
6.2.5.1.3. Comparison
6.2.5.2. Minivida
6.2.5.2.1. The full program
6.2.5.2.2. Remove the staircase
6.2.5.2.3. Minimal instructions
6.2.6. Avida is intelligently designed
6.2.7. Beating a dead organism
6.3. Metabiology
6.3.1. The essence of halting
6.3.2. On with the search
6.3.3. The math: intelligent design in metabiology
6.3.4. Resources
6.4. Conclusion: Sweeping a Dirt Floor
6.4.1. Evolving a Steiner tree
6.4.2. Time for evolution
6.4.3. Finis
Notes
7. Measuring Meaning: Algorithmic Specified Complexity
7.1. The Meaning of Meaning
7.2. Conditional KCS Complexity
7.3. Defining Algorithmic Specified Complexity (ASC)
7.3.1. High ASC is rare
7.4. Examples of ASC
7.4.1. Extended alphanumerics
7.4.2. Poker
7.4.3. Snowflakes
7.4.4. ACS in the Game of Life
7.4.4.1. The Game of Life
7.4.4.2. Cataloging context
7.4.4.2.1. Still lifes and oscillators
7.4.4.2.2. Gliders
7.4.4.2.3. Higher complexity
7.4.4.3. Measuring ASC in bits
7.4.4.3.1. Measuring I(X)
7.4.4.3.2. Measuring the conditional KCS complexity in bits
7.4.4.3.3. Oscillator ASC
7.4.4.4. Measuring meaning
7.5. Meaning is in the Eye of the Beholder
Notes
8. Intelligent Design & Artificial Intelligence
8.1. Turing & Lovelace: One is Strong and the Other Ones Dead
8.1.1. Turings failure
8.1.2. The Lovelace test and ID
8.1.3. Flash of genius
8.2. ID & the Unknowable
8.2.1. Darwinian evolutionary programs have failed the Lovelace test
8.3. Finis
Notes
9. Appendices
9.1. Acronym List
9.2. Variables
9.3. Notation
Index
PREFACE
My theory of evolution is that Darwin was adopted
Steven Wright
Science has made great strides in modeling space, time, mass, and energy but has done little to definitively model the obvious meaningful information ubiquitous in our universe. Today, information theory is used to measure the storage capacity of a Blu-ray disc or for describing the bandwidth of a Wi-Fi connection. Yet the difficulty associated with the design of the Blu-ray contents and the meaning of data transmitted across the Wi-Fi connection are not addressed. New results in information theory now allow meaning and design difficulty to be measured. Explaining the foundation of this exciting theory at an accessible level is our goal in Introduction to Evolutionary Informatics.
Evolutionary models to date point strongly to the necessity of design. Indeed, all current models of evolution require information from an external designer in order to work. All current evolutionary models simply do not work without tapping into an external information source.
Foundation
This monographs contents stem from the seminal works of one of your humble co-authors, WilliamA. Dembski,1 and subsequent edited volumes.2 The authors have penned numerous papers and book chapters that contain the foundational development of material for this monograph.3 Links to many of these papers are available on our website EvoInfo.org. In certain places in this monograph we have lifted prose and figures from some of these papers, in some cases verbatim. We have attempted in all cases to make specific reference, but might have missed some.
As witnessed by this body of work, the material in this monograph stands on firm ground. Peer-reviewed papers, though, are written at a level where only dedicated nerds can understand them. This monograph serves two purposes. The first is explanation of evolutionary informatics at a level accessible to the well-informed reader. Secondly we believe a la Romans 1:20 and like verses that the implications of this work in the apologetics of perception of meaning are profound.
The Math Herein and the Symbol
Although we have attempted to minimize the mathematics in this book, its use in some areas is necessary. In such cases, we have isolated the math and give as clear an explanation of the underlying reasoning as possible. The math material can be understood with rudimentary knowledge of
simple logarithms,
elementary probability,
elementary concepts in statistics such as averages (or sample means) being estimates of distribution means,
numbers represented in binary (base 2), and
simple Boolean logic operations such as and, or, not, nand, nor, xor, etc.
To aid those who wish to read the book more quickly or who are not interested in mathematical details, sections marked with a dagger () can be skipped. Some mathematical details are also relegated to footnotes and are also marked with a dagger.
Footnotes and Endnotes
Generally notes at the end of the chapters are references whereas footnotes contain elaboration on the chapter story. For fast or casual reading, the footnotes can be skipped.
Chapter Summaries
Chapter 1: Introduction
Summary: Rather than placing a theory or ideology on the throne like a Queen as scientists and philosophers often do, engineers make the Queen come down from the throne and scrub the floor. And if she doesnt work, she is fired.
Scientists once thought evolution models running on fast computers would someday confirm evolution. The opposite has happened. Prophets of computer-based demonstration of undirected evolution failed to take into account Borels law and the Conservation of Information. Borels law dictates that events described by a sufficiently small probability are impossible events. For example, there is a small probability that you will experience quantum tunneling through the chair in which you sit. The probability is so small, however, that we can categorize the event as impossible.
Chapter 2: Information: What is It?
Summary: Information is neither matter nor energy. It stands as an independent component of nature.