Creative Evolutionary Systems

Creative Evolutionary Systems

1st Edition - July 16, 2001

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  • Authors: David Corne, Peter Bentley
  • eBook ISBN: 9780080503370

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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.

Key Features

* 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

Table of Contents

  • About the Editors


    By Margaret Boden



    An Introduction to Creative Evolutionary Systems

    By Peter J. Bentley and David W. Corne


    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



    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


    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


    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



    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


    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



    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



    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




    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


    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


    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



    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



    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


    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


    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



    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



    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



    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


    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



    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


    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



    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



Product details

  • No. of pages: 576
  • Language: English
  • Copyright: © Morgan Kaufmann 2001
  • Published: July 16, 2001
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9780080503370

About the Authors

David Corne

David W. Corne is a reader in evolutionary computation (EC) at the University of Reading. His early research on evolutionary timetabling (with Peter Ross) resultedin the first freely available and successful EC-based general timetabling programfor educational and other institutions. His later EC work has been in suchareas as DNA pattern mining, promoter modeling, phylogeny, scheduling, layoutdesign, telecommunications, data mining, algorithm comparison issues, and multiobjectiveoptimization. Recent funded work (with Douglas Kell) applies EC directlyto the in vivo optimization of proteins. He is an associate editor of the IEEETransactions on Evolutionary Computation and a founding co-editor of the Journal ofScheduling. Dr. Corne is on the editorial boards of Applied Soft Computing and the InternationalJournal of Systems Science, and he serves on a host of international conferenceprogram committees. Other recent edited books include New Ideas in Optimization(with Marco Dorigo and Fred Glover), Telecommunications Optimization: Heuristic andAdaptive Techniques (with Martin Oates and George Smith), and Creative EvolutionarySystems (with Peter Bentley). He is also a director of Evosolve (United Kingdomregistered charity number 1086384, with Jeanne Lynch-Aird, Paul Marrow, GlenysOates, and Martin Oates), a nonprofit organization that promotes the use of advancedcomputation technologies to enhance the quality of life.

Affiliations and Expertise

University of Reading

Peter Bentley

Peter J. Bentley is a Honorary Research Fellow at University College London, known for his research covering all aspects of EC, including multiobjective optimization, constraint handling, artificial immune systems, computational embryology and more, and applied to diverse applications including floor-planning, control, fraud-detection, and music composition. He speaks regularly at international conferences, and is a consultant, convenor, chair and reviewer for workshops, conferences, journals and books on Evolutionary Design and Evolutionary Computation. He has been a guest editor of special issues on Evolutionary Design and Creative Evolutionary Systems in journals, and is the editor of the book Evolutionary Design by Computers (MKP) and is the author of the popular science book, Digital Biology, to publish in May 2001.

Affiliations and Expertise

University College London, U.K.

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