Creative Evolutionary Systems

About the Editors

Foreword

By Margaret Boden

Contributors

Preface

An Introduction to Creative Evolutionary Systems

By Peter J. Bentley and David W. Corne

Introduction

AI and Creativity

Evolutionary Computation

Creative Evolutionary Systems

Is Evolution Creative?

PART I - Evolutionary Creativity

Chapter 1 - Creativity in Evolution: Individuals, Interactions, and Environments

By Tim Taylor

1.1 Introduction

1.2 Creativity and Opened-Ended Evolution

1.3 Design Issues

1.3.1 Von Neumann's Architecture for Self-Reproduction

1.3.2 Tierra

1.3.3 Implicit versus Explicit Encoding

1.3.4 Ability to Perform Other Tasks

1.3.5 Embeddedness in the Arena of Competition and Richness of Interactions

1.3.6 Materiality

1.4 A Full Specification For An Open-Ended Evolutionary Process

1.4.1 Waddington's Paradigm for an Evolutionary Process

1.5 Conclusions

Acknowledgments

References

Chapter 2 - Recognizability of the Idea: The Evolutionary Process of Argenia

By Celestino Soddu

2.1 Introduction

2.2 Recognizability, Identity, And Complexity

2.3 Evolutionary Codes: Artificial DNA

2.4 Natural/Artificial Complexity

2.5 Giotto, A Medieval Idea In Evolution

2.6 Rome, Future Scenarios

2.7 Basilica, Generative Software To Design Complexity

2.8 Madrid and Milan, Generated Architecture

2.9 Argenìa, The Natural Industrial Object, And The Artificial Uniqueness Of Species

2.10 Argenìc Art: Picasso

2.11 Conclusions

References

Chapter 3 - Breeding Aesthetic Objects: Art and Artificial Evolution

By Mitchell Whitelaw

3.1 Introduction

3.2 Breeding Aesthetic Objects

3.2.1 A Case Study—Steven Rooke

3.3 Breeding and Creation

3.3.1 Creative Agency and the Breeding Process

3.3.2 The Evolved Aesthetic Object

3.4 Limits

3.5 Driessens and Verstappen—An Alternative Approach

3.6 Conclusions

References

Chapter 4 - The Beer Can Theory of Creativity

By Liane Gabora

4.1 Introduction

4.2 Culture As An Evolutionary Process

4.2.1 Variation and Convergence in Biology and Culture

4.2.2 Is More Than One Mind Necessary for Ideas to Evolve?

4.2.3 Meme and Variations: A Computer Model of Cultural Evolution

4.2.4 Breadth-First versus Depth-First Exploration

4.2.5 Dampening Arbitrary Associations and Forging Meaningful Ones

4.3 Creativity as The Origin Of Culture

4.3.1 Theoretical Evidence

4.3.2 Archeological Evidence

4.3.3 Evidence from Animal Behavior

4.4 What Caused the Onset of Creativity?

4.5 Conclusions

Acknowledgments

References

PART II Evolutionary Music

Chapter 5 - GenJam: Evolution of a Jazz Improviser

By John A. Biles

5.1 Introduction

5.2 Overview and Architecture

5.3 Representations

5.4 Genetic Operators and Training

5.4.1 Crossover

5.4.2 Musically Meaningful Mutation

5.5 Real-Time Interaction

5.6 Conclusions

References

Chapter 6 - On the Origins and Evolution of Music in Virtual Worlds

By Eduardo Reck Miranda

6.1 Introduction

6.2 Evolutionary Modeling

6.2.1 Transformation and Selection

6.2.2 Coevolution

6.2.3 Self-organization

6.2.4 Level Formation

6.3 Evolving Sound With Cellular Automata

6.3.1 The Basics of Cellular Automata

6.3.2 The Cellular Automaton Used in Our System

6.3.3 The Synthesis Engine

6.4 Commentary On The Results

6.5 Conclusions

Acknowledgments

References

Chapter 7 - Vox Populi: Evolutionary Computation for Music Evolution

By Artemis Moroni, Jônatas Manzolli, Fernando Von Zuben, and Ricardo Gudwin

7.1 Introduction

7.2 Sound Attributes

7.3 Evolutionary Musical Cycle

7.3.1 The Voices Population

7.3.2 The Rhythm of the Evolution

7.4 Fitness Evaluation

7.4.1 The Consonance Criterion

7.4.2 Melodic Fitness

7.4.3 Harmonic Fitness

7.4.4 Voice Range Criterion

7.4.5 Musical Fitness

7.5 Interface And Parameter Control

7.6 Experiments

7.7 Conclusions

Acknowledgments

References

Chapter 8 - The Sound Gallery—An Interactive A-Life Artwork

By Sam Woolf and Adrian Thompson

8.1 Introduction

8.2 Evolvable Hardware

8.2.1 Reconfigurable Chips

8.3 Gallery Setup

8.3.1 Setting

8.3.2 Sensing Systems

8.4 Contextualization: Artificial Life and Art

8.4.1 Evolutionary Algorithms and Visual Arts

8.4.2 Evolutionary Algorithms and Music

8.4.3 Interactive Genetic Art

8.4.4 Interactive, Adaptive, and Autonomous (Nongenetic) Artworks

8.5 The Sound Gallery Algorithms

8.5.1 Two-Phase Hill-Climbing/ Island Model GA

8.5.2 Hill-climbing Phase

8.5.3 Island Model Genetic Algorithm Phase

8.5.4 The Need for Aging

8.5.5 Encoding Scheme

8.5.6 The Fitness Function

8.5.7 galSim

8.6 The Experiment

8.6.1 Results

8.7 Conclusions

Acknowledgments

References

Contents

PART III Creative Evolutionary Design

Chapter 9 - Creative Design and the Generative Evolutionary Paradigm

By John Frazer

9.1 Introduction

9.2 The Adaptive Model From Nature

9.3 The Generative Evolutionary Paradigm

9.4 Problems With The Paradigm

9.5 Concept Seeding Approach

9.6 The Reptile Demonstration

9.7 Universal State Space Modeler

9.8 Logic Fields

9.9 Returning to the Analogy with Nature

9.10 Conclusions

References

Chapter 10 - Genetic Programming: Biologically Inspired Computation That Exhibits Creativity in Producing Human-Competitive Results

By John R. Koza, Forrest H. Bennett III, David Andre, and Martin A. Keane

10.1 Introduction

10.2 Inventiveness And Creativity

10.3 Genetic Programming

10.4 Applying Genetic Programming To Circuit Synthesis

10.4.1 Campbell 1917 Ladder Filter Patent

10.4.2 Zobel 1925 "M-Derived Half Section" Patent

10.4.3 Cauer 1934-1936 Elliptic Filter Patents

10.4.4 Amplifier, Computational, Temperature-Sensing, Voltage Reference, and Other Circuits

10.5 Topology, Sizing, Placement, and Routing Of Circuits Contents

10.6 Automatic Synthesis Of Controllers By Means Of Genetic Programming

10.6.1 Robust Controller for a Two-Lag Plant

10.7 The Illogical Nature Of Creativity And Evolution

10.8 Conclusions

References

Chapeter 11 - Toward a Symbiotic Coevolutionary Approach to Architecture

By Helen Jackson

11.1 Introduction

11.2 Lindenmayer Systems

11.2.1 Example L-Systems

11.2.2 The Isospatial Grid

11.2.3 Spatial Embryology

11.3 Artificial Selection

11.3.1 The Eyeball Test

11.4 Single-Goal Evolution

11.4.1 "Generic Function" as Fitness Function

11.4.2 Evolution toward Low i-Values

11.4.3 Structural Stability

11.4.4 Architecture As a Multigoal Task

11.4.5 Dual-Goal Evolution

11.5 Representation, Systems, And Symbiosis

11.5.1 Coevolution

11.5.2 Naïve Architectural Form Representation

11.5.3 Spatial Embryology

11.6 Conclusions

Acknowledgments

References

Chapter 12 - Using Evolutionary Algorithms to Aid Designers of Architectural Structures

By Peter von Buelow

12.1 Introduction

12.2 Analysis Tools Vs. Design Tools

12.3 Advantages Of Evolutionary Systems In Design Contents

12.3.1 Use of Populations

12.3.2 Recombination and Mutation

12.3.3 Wide Search of Design Space

12.3.4 No Knowledge of the Objective Function

12.3.5 Imitation of Human Design Process

12.3.6 Can Learn from Designer

12.4 Characteristics of an IGDT

12.4.1 Definition of the IGDT Concept

12.4.2 Relation of IGDT to Design Process

12.5 Mechanics of an IGDT

12.6 IGDT Operation

12.6.1 Problem Definition

12.6.2 Initial IGDT Generation

12.6.3 Initial Generation with Designer Selection/Interaction

12.6.4 Second-Generation IGDT Response

12.6.5 Second-Generation Designer Interaction

12.6.6 Third Generation

12.7 Conclusions

Acknowledgments

References

PART IV Evolutionary Art

Chapter 13 - Eons of Genetically Evolved Algorithmic Images

By Steven Rooke

13.1 Introduction

13.2 Using GP for Art

13.2.1 Genetic Variation

13.2.2 Genetic Library

13.2.3 Functions and Node Internals

13.2.4 A Typical Run

13.3 Horizon Lines And Fantasy Landscapes

13.4 Genetic Fractals

13.4.1 Second-Order Subtleties of Orbit Trajectories during Iteration in the Complex Plane

13.5 The Genetic Cross Dissolve

13.6 What Is It?

13.6.1 Constraints of Color and Form

13.6.2 A Joyride for the Visual Cortex?

13.6.3 Approaching the Organic

13.7 Conclusions

References

Chapter 14 - Art, Robots, and Evolution as a Tool for Creativity

By Luigi Pagliarini and Henrik Hautop Lund

14.1 Introduction

14.2 The Social Context Of Electronics

14.2.1 Where Electronics Acts

14.2.2 How Technology Influences Art (the World)

14.2.3 How Technology Gets Feedback (from Art and the World)

14.3 What Artist?

14.3.1 Two Different Concepts or Aspects of the Artist

14.3.2 Art and Human Language: The "Immaterial" Artist

14.3.3 Art and Human Technique: The "Material" Artist

14.4 Electronic Art

14.4.1 A New Electronic Space

14.4.2 The "Material" Electronic Artist

14.4.3 The "Immaterial" Artist and the Uses of Electronics

14.4.4 Example—The Artificial Painter

14.5 Alive Art

14.5.1 Other Artistic Movements Based on Electronics

14.5.2 Alive Art

14.5.3 The Aliver

14.5.4 The "Alive Art Effect"

14.5.5 Example—LEGO Robot Artists

14.6 Conclusions

References

Chapter 15 - Stepping Stones in the Mist

By Paul Brown

15.1 Introduction

15.2 On My Approach as an Artist—A Disclaimer

15.3 Major Influences

15.4 Historical Work—1960s and 1970s

15.5 Early Computer Work

15.6 Recent Work

15.7 Current And Future Directions

15.8 Conclusions

Acknowledgments

References

Chapter 16 - Evolutionary Generation of Faces 409

By Peter J. B. Hancock and Charlie D. Frowd

16.1 Introduction

16.1.1 Eigenfaces

16.1.2 Evolutionary Face Generator System

16.2 Testing

16.2.1 Apparatus

16.2.2 Generation of Face Images

16.2.3 Evolutionary Algorithm

16.2.4 Participants

16.3 Results

16.4 Discussion

16.5 Conclusions

Acknowledgments

References

Chapter 17 - The Escher Evolver: Evolution to the People

By A. E. Eiben, R. Nabuurs, and I. Booij

17.1 Introduction

17.2 The Mathematical System Behind Escher's Tiling

17.3 Evolutionary Algorithm Design

17.3.1 Representation

17.3.2 Ground Shape and Transformation System

17.3.3 Genetic Operators: Mutation and Crossover

17.3.4 Selection Mechanism

17.4 Implementation and The Working of The System

17.4.1 Stand-Alone Version

17.4.2 First Networked Version

17.4.3 Second Networked Version

17.5 Conclusions

Acknowledgments

References

PART V Evolutionary Innovation

Chapter 18 - The Genetic Algorithm as a Discovery Engine: Strange Circuits and New Principles

By Julian F. Miller, Tatiana Kalganova, Natalia Lipnitskaya, and Dominic Job

18.1 Introduction

18.2 The Space of All Representations

18.3 Evolutionary Algorithms That Assemble Electronic Circuits From A Collection of Available Components

18.3.1 Binary Circuit Symbols

18.3.2 Multiple-Valued Circuits

18.4 Results

18.4.1 One-Bit Adder

18.4.2 Two-Bit Adder

18.4.3 Two-Bit Multiplier

18.4.4 Three-Bit Multiplier

18.4.5 Multiple-Valued One-Digit Adder with Carry

18.5 Fingerprinting and Principle Extraction

18.6 Conclusions

References

Chapter 19 - Discovering Novel Fighter Combat Maneuvers: Simulating Test Pilot Creativity

By R. E. Smith, B. A. Dike, B. Ravichandran, A. El-Fallah, and R. K. Mehra

19.1 Introduction

19.2 Fighter Aircraft Maneuvering

19.3 Genetics-Based Machine Learning

19.3.1 Learning Classifier Systems

19.3.2 The LCS Used Here

19.4 "One-Sided Learning" Results

19.5 "Two-Sided Learning" Results

19.6 Differences In Goals And Techniques

19.6.1 Implications of This Goal

19.7 Conclusions

Acknowledgments

References

Chapter 20 - Innovative Antenna Design Using Genetic Algorithms

By Derek S. Linden

20.1 Introduction

20.2 Antenna Basics

20.3 Conventional Designs and Unconventional Applications: The Yagi-Uda Antenna

20.4 Unconventional Designs and Conventional Applications: Crooked-Wire And Treelike Genetic Antennas

20.4.1 The Crooked-Wire Genetic Antenna

20.4.2 Treelike Genetic Antennas

20.5 Conclusions

References

Chapter 21 - Evolutionary Techniques in Physical Robotics

By Jordan B. Pollack, Hod Lipson, Sevan Ficici, Pablo Funes, Greg Hornby, and Richard A. Watson

21.1 Introduction

21.2 Coevolution

21.3 Research Thrusts

21.4 Evolution In Simulation

21.5 Buildable Simulation

21.6 Evolution and Construction of Electromechanical Systems

21.7 Embodied Evolution

21.8 Conclusions

Acknowledgments

References

Chapter 22 - Patenting of Novel Molecules Designed via Evolutionary Search

By Shail Patel, Ian Stott, Manmohan Bhakoo, and Peter Elliott

22.1 Introduction

22.2 Design Cycle

22.3 Hypothesis: Mechanism Of Action

22.4 Experimental Measures And Modeling Techniques

22.4.1 Molecular Modeling

22.4.2 Neural Networks

22.5 Evolution

22.6 Patent Application

22.6.1 Comparing Patent Spaces

22.7 Conclusions

References

Index

The use of evolution for creative problem solving is one of the most exciting and potentially significant areas in computer science today. Evolutionary computation is a way of solving problems, or generating designs, using mechanisms derived from natural evolution. This book concentrates on applying important ideas in evolutionary computation to creative areas, such as art, music, architecture, and design. It shows how human interaction, new representations, and approaches such as open-ended evolution can extend the capabilities of evolutionary computation from optimization of existing solutions to innovation and the generation of entirely new and original solutions.

This book takes a fresh look at creativity, exploring what it is and how the actions of evolution can resemble it. Examples of novel evolved solutions are presented in a variety of creative disciplines. The editors have compiled contributions by leading researchers in each discipline.

If you are a savvy and curious computing professional, a computer-literate artist, musician or designer, or a specialist in evolutionary computation and its applications, you will find this a fascinating survey of the most interesting work being done in the area today.

• Explores the use of evolutionary computation to generate novel creations including contemporary melodies, photo-realistic faces, jazz music in collaboration with a human composer, architectural designs, working electronic circuits, novel aircraft maneuvers, two- and three-dimensional art, and original proteins.
• Presents resulting designs in black-and-white and color illustrations.
• Includes a twin-format audio/CD-ROM with evolved music and hands-on activities for the reader, including evolved images, animations, and source-code related to the text.
• Describes in full the methods used so that readers with sufficient skill and interest can replicate the work and extend it.
• Is written for a general computer science audience, providing coherent and unified treatment across multiple disciplines.

Computer scientists and students, artists, musicians, and specialists in evolutionary computation