Introduction To Evolutionary Informatics

by Robert J Marks Ii, William A Dembski, Winston Ewert

Overview

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.

Product Details

ISBN-13:	9789813142145
Publisher:	World Scientific Publishing Company, Incorporated
Publication date:	04/19/2017
Pages:	332
Product dimensions:	6.00(w) x 9.10(h) x 1.20(d)

Table of Contents

Preface xiii

About the Authors xxiii

1 Introduction 1

1.1 The Queen of Scientists & Engineers 2

1.2 Science and Models 3

1.2.1 Computer models 4

1.2.2 The improbable and the impossible 4

Notes 5

2 Information: What Is It? 7

2.1 Defining Information 7

2.2 Measuring Information 10

2.2.1 KCS complexity 10

2.2.1.1 KCS information using prefix free programs 13

2.2.1.2 Random programming and the Kraft inequality 15

2.2.1.3 Knowabilily 17

2.2.1.4 Application 18

2.2.2 Shannon information 18

2.2.2.1 Twenty questions: Interval halving and bits 21

2.2.2.2 Shannon information applied to interval halving 22

2.3 Remarks 26

Notes 26

3 Design Search in Evolution and the Requirement of Intelligence 29

3.1 Design as Search 29

3.1.1 WD-40™ and Formula 409™ 30

3.1.2 Tesla. Edison and domain expertise 30

3.2 Design by Computer 31

3.3 Designing a Good Pancake 32

3.3.1 A search for a good pancake #1 32

3.3.2 A search for a good pancake #2: Cooking times plus range setting 35

3.3.3 A search for a good pancake #3: More recipe variables 36

3.3.4 A search for a good pancake #4: Simulating pancakes on a computer with an artificial tongue using a single agent 38

3.3.5 A search for a good pancake #5: Simulating pancakes on a computer with an artificial tongue using an evolutionary search 40

3.4 Sources of Knowledge 41

3.4.1 Designing antennas using evolutionary computing 43

3.5 The Curse of Dimensionality & the Need for Knowledge 46

3.5.1 Will Moore ever help9 How about Grover? 47

3.6 Implicit Targets 48

3.7 Skeptic Fallibility 50

3.7.1 Loss of function 52

3.7.2 Pareto optimization and optimal sub-optimality 52

3.7.3 A man-in-the-loop sneaks in active information 55

3.7.3.1 Evolving Tic-Tac-Toe to checkers to chess 55

3.7.3.2 Replacing the man-in-the loop with a computer-in-the-loop 55

3.8 A Smorgasbord of Search Algorithms 56

3.9 Conclusions 59

Notes 59

4 Determinism in Randomness 67

4.1 Bernoulli's Principle of Insufficient Reason 69

4.1.1 "Nothing is that which rocks dream about" 69

4.1.2 Bernoulli's Principle (PiOIR) 70

4.1.2.1 Examples 71

4.1.2.2 Criticisms of Bernoulli's principle 71

4.1.2.2.1 Model variations 72

4.1.2.2.2 Vague definitions & ambiguity: Bert rand's paradox 78

4.1.2.2.3 Continuous versus discrete probability 81

4.2 The Need for Noise 86

4.2.1 Fixed points in random events 87

4.2.2 Importance sampling 90

4.2.3 Limit cycles, strange attractors & tetherball 92

4.3 Basener's ceiling 93

4.3.1 Tierra 95

4.3.2 The edge of evolution 98

4.4 Final Comments 99

Notes 100

5 Conservation of Information in Computer Search 105

5.1 The Genesis 105

5.2 What is Conservation of Information? 107

5.2.1 Deceptive counterexamples 109

5.2.2 What does learning have to do with design? 112

5.2.2.1 Sumo wrestlers can't play basketball 112

5.2.3 A man-in-the-loop sneaks in active information 117

5.2.3.1 Back room tuning 119

5.3 The Astonishing Cost of Blind Search in Bits 120

5.3.1 Analysis 121

5.3.2 The cost 123

5.4 Measuring Search Difficulty in Bits 125

5.4.1 Endogenous information 125

5.4.1.1 Two special cases 127

5.4.1.2 Endogenous information of the Cracker Barrel puzzle 128

5.4.2 Active information 130

5.4.2.1 Examples of sources of knowledge 134

5.4.2.2 Active information per query 135

5.4.2.2.1 A subtle distinction 136

5.4.2.3 Examples of active information 138

5.4.2.3.1 The Cracker Barrel puzzle 138

5.4.2.3.2 The Monte Hall problem 140

5.4.2.3.3 A sibling problem 141

5.4.2.3.4 Multiple queries 144

5.4.3 Mining active information from oracles 145

5.4.3.1 The Hamming oracle 145

5.4.3.2 Weasel ware and variations of information mining 151

5.5 Sources of Information in Evolutionary Search 155

5.5.1 Population 155

5.5.2 Mutation rate 156

5.5.3 Fitness landscapes 156

5.5.3.1 Initialization 160

5.6 Stairstep Information & Transitional Functional Viability 160

5.64 Baby steps 161

5.6.2 Developmental functionality and irreducible complexity 161

5.6.2.1 Example: Using an EAR_TATTER_ 164

5.6.2.2 Analysis 165

5.6.3 Irreducible complexity 167

5.7 Coevolution 167

5.8 The Search for the Search 171

5.8.1 An example 171

5.8.2 The problem 173

5.8.2.1 The weak case 174

5.8.2.2 The strict case 174

5.8.3 Proofs 175

5.8.3.1 Preliminaries 175

5.8.3.2 The weak case 177

5.8.3.3 The strict case 178

5.9 Conclusion 180

Notes 181

6 Analysis of Some Biologically Motivated Evolutionary Models 187

6.1 EV: A Software Model of Evolution 188

6.1.1 EV structure 188

6.1.2 EV vivisection 192

6.1.3 Information sources resident in EV 194

6.1.4 The search 198

6.1.4.1 Search using the number cruncher 198

6.1.4.2 Evolutionary search 198

6.1.4.3 EV and stochastic hill climbing 199

6.1.4.4 Mutation Tate 199

6.1.5 EV ware 200

6.1.6 The diagnosis 201

6.2 Avida: Stair Steps to Complexity Using NAND Logic 205

6.2.1 Kitzmiller et at versus Dover area school district 206

6.2.2 Boolean logic 207

6.2.3 NAND logic 209

6.2.3.1 Logic synthesis using NAND gates 209

6.2.4 The Avida organism and its health 213

6.2.5 Information analysis of Avida 217

6.2.5.1 Performance 219

6.2.5.1.1 The evolutionary approach 219

6.2.5.1.2 The ratchet approach 220

6.2.5.1.3 Comparison 221

6.2.5.2 Minivida 221

6.2.5.2.1 The full program 223

6.2.5.2.2 Remove the staircase 224

6.2.5.2.3 Minimal instructions 225

6.2.6 Avida is intelligently designed 227

6.2.7 Beating a dead organism 230

6.3 Metabiology 231

6.3.1 The essence of halting 233

6.3.2 On with the search 234

6.3.3 The math: "intelligent design" in metabiology 236

6.3.4 Resources 240

6.4 Conclusion: Sweeping a Dirt Floor 241

6.4.1 Evolving a Sterner tree 241

6.4.2 Time for evolution 242

6.4.3 Finis 243

Notes 243

7 Measuring Meaning: Algorithmic Specified Complexity 251

7.1 The Meaning of Meaning 251

7.2 Conditional KCS Complexity 253

7.3 Defining Algorithmic Specified Complexity (ASC) 255

7.3.1 High ASC is rare 256

7.4 Examples of ASC 257

7.4.1 Extended alphanumerics 257

7.4.2 Poker 261

7.4.3 Snowflakes 262

7.4.4 ACS in the Game of Life 265

7.4.4.1 The Game of Life 265

7.4.4.2 Cataloging context 269

7.4.4.2.1 Still lifes and oscillators 271

7.4.4.2.2 Gliders 273

7.4.4.2.3 Higher complexity 275

7.4.4.3 Measuring ASC in bits 276

7.4.4.3.1 Measuring I(X) 276

7.4.4.3.2 Measuring the conditional KCS complexity in bits 277

7.4.4.3.3 Oscillator ASC 277

7.4.4.4 Measuring meaning 278

7.5 Meaning is in the Eye of the Beholder 278

Notes 279

8 Intelligent Design & Artificial Intelligence 281

8.1 Turing & Lovelace: One is Strong and the Other One's Dead 282

8.1.1 Turing's failure 282

8.1.2 The Lovelace test and ID 284

8.1.3 "Flash of genius" 285

8.2 ID & the Unknowable 287

8.2.1 Darwinian evolutionary programs have failed the Lovelace test 288

8.3 Finis 288

Notes 288

9 Appendices 291

9.1 Acronym List 291

9.2 Variables 292

9.3 Notation 293

Index 295