Introduction to Evolutionary Informatics
By (author): Robert J Marks II (Baylor University, USA), William A Dembski (Evolutionary Informatics Lab, USA), Winston Ewert (Evolutionary Informatics Lab, USA)
About this book
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:
History
Foundation
The Math Herein and the † Symbol
Introduction::
The Queen of Scientists and Engineers
Science & Models
Computer Models
The Improbable and the Impossible
Information. What is It?:
Defining Information
Measuring Information
Kolmogorov–Chaitin–Solomonov (KCS) Information
KCS Information Using Prefix Free Programs
Random Programming and the Kraft Inequality
Knowability
Application
Shannon Information
Twenty Questions: Interval Halving and Bits
Shannon Information Applied to Interval Halving
Design Search in Evolution and the Requirement of Intelligence:
Design as Search
WD-40 and Formula 409
Tesla, Edison and Domain Expertise
Design by Computer
Designing a Good Pancake
A Search for a Good Pancake
Cooking Times Plus Range
More Recipe Variables
Simulating Pancakes on a Computer with an Artificial Tongue
Sources of Knowledge
Designing Antennas Using Evolutionary Computing
The Curse of Dimensionality & the Need For Knowledge
Will Moore Ever Help? How About Grover?
Implicit Targets
Skeptic Fallibility
Loss of Function
Pareto Optimization and Optimal Suboptimality
A Man-in-the-Loop Sneaks in Active Information
Evolving Tic-Tac-Toe to Checkers to Chess
Replacing the Man-in-the-Loop with a Computer-in-the-Loop
A Smörgåsbord of Search Algorithms
Determinism in Randomness:
Bernoulli's Principle of Insufficient Reason
"Nothing is that Which Rocks Dream About."
Bernoulli's Principle (PrOIR)
Criticisms of Bernoulli's Principle
Model Variations
Vague Definitions & Ambiguity: Bertrand's Paradox
Continuous vs. Discrete Probability
The Need For Noise
Fixed Points in Random Events
Importance Sampling
Limit Cycles, Strange Attractors & Tetherball
Basener's Ceiling
Tierra
The Edge of Evolution
Conservation of Information in Evolutionary Processes:
The Genesis
What is Conservation of Information?
Deceptive Counterexamples
What Does Learning Have to do with Design?
Sumo Wrestlers Can't Play Basketball
A Man-in-the-Loop Sneaks in Active Information
Back Room Tuning
The Astonishing Cost of Blind Search in Bits
Analysis
The Cost
Measuring Search Difficulty in Bits
Endogenous Information
Two Special Cases
Endogenous Information of the Crackle Barrel Puzzle
Active Information
Examples of Sources of Knowledge
Active Information per Query
A Subtle Distinction
Examples of Active Information
The Cracker Barrel Puzzle
The Monte Hall Problem
A Sibling Problem
Multiple Queries
Mining Active Information From Oracles
The Hamming Oracle
Weasel Ware and Variations of Information Mining
Sources of Information in Evolutionary Search
Population
Mutation Rate
Fitness Landscapes
Initialization
Stair Step Information & Transitional Functional Viability
Baby Steps
Developmental Functionality and Irreducible Complexity
Example: Using an EAR_TATTER_
Irreducible Complexity
Coevolution
The Search for the Search
The Problem
The Weak Case
The Strict Case
Proofs
Preliminaries
Analysis of Some Evolutionary Models:
EV: A Software Model of Evolution
EV Structure
EV Vivisection
Information Sources Resident in EV
The Search
Search Using the Number Cruncher
Evolutionary Search
EV and Stochastic Hill Climbing
Mutation Rate
EV Ware
The Diagnosis
AVIDA: Stair Steps to Complexity Using NAND Logic
Kitzmiller et al. v. Dover Area School District
Boolean Logic
NAND Logic
Logic Synthesis Using NAND Gates
The AVIDA Organism and Its Health
Information Analysis of Avida
The Evolutionary Approach
The Ratchet Approach
Minivida
The Full Program
Remove the Staircase
Minimal Instructions
Avida is Intelligently Designed
Beating a Dead Organism
Metabiology
The Essence of Halting
On With the Search
The Math: "Intelligent Design" in Metabiology
Sweeping a Dirt Floor
Evolving a Steiner Tree
Time for Evolution
Meaning of Meaning:
Algorithmic Specified Complexity: Conditional KCS Complexity
Defining Algorithmic Specified Complexity
† High ASC is Rare
Examples of Algorithmic Specified Complexity
Extended Alphanumerics
Poker
Snowflakes
ASC in the Game of Life
The Game of Life
Cataloging Context
Still Life and Oscillators
Gliders
Higher Complexity
Measuring Information Content
Measuring
Measuring the Conditional KCS Complexity in Bits
Oscillator ASC
Measuring Meaning
Meaning is in the Eye of the Beholder
Intelligent Design & Artificial Intelligence:
Turing & Lovelace: One is Strong and the Other One's Dead
Turing's Failure
The Lovelace Test and Intelligent Design
"Flash of Genius"
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.
About the Author(s)
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 Senior Fellow with Seattle's Discovery Institute, Washington, USA. 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.
Available: January 30, 2017 World Scientific Publishing Co e Amazon Books.
