Creative Evolutionary Systems
Buku ini diterbitkan Tahun 2002 oleh Academic Press, USA adalah buku edisi Pertama.
Judul: Creative Evolutionary Systems
Oleh: Peter J. Bentley, et al (Editor).
Penerbit: Academic Press, USA
Tahun: 2002
Jumlah Halaman: 609 hal.
Editor:
Peter J. Bentley adalah Anggota Kehormatan Research Fellow di UCL, yang dikenal untuk penelitian produktif mencakup semua aspek EC, termasuk optimasi multiobjective, penanganan kendala, sistem kekebalan tubuh buatan, embriologi komputasi, dan banyak lagi, dan diterapkan untuk aplikasi yang beragam termasuk lantai-perencanaan, pengendalian, deteksi penipuan, dan Komposisi musik. Dia berbicara secara teratur pada konferensi internasional dan konsultan, Convenor, kursi, dan resensi untuk lokakarya, konferensi, jurnal, dan buku-buku tentang desain evolusioner dan perhitungan evolusi. Dia editor tamu istimewa dalam masalah pada desain evolusioner dan sistem evolusi kreatif dalam jurnal dan adalah editor dari buku Evolutionary Desain oleh Komputer (kata pengantar oleh Richard Dawkins) dan penulis buku populer ilmu Digital Biologi.
David W. Corne kuliah dan berkonsultasi di EC di University of Reading. Penelitian awal pada penjadwalan evolusi (dengan Peter Ross) mengakibatkan pertama yang tersedia secara bebas dan Program penjadwalan umum untuk pendidikan dan lainnya sukses EC berbasis lembaga. Kemudian kerja EC telah di tambah protein, penelitian genom manusia, kedokteran, penjadwalan, desain tata letak, telekomunikasi, data mining, algoritma masalah perbandingan, optimasi multiobjective, dan lain-lain. Dia adalah associate editor dari [EEE Transaksi di Evolutionary Computation, seorang salah seorang editor pendiri Journal Penjadwalan, di Dewan Ulasan Intelijen Terapan, dan ia muncul di sejumlah komite program konferensi internasional. Buku yang diedit baru lainnya termasuk Ide Baru di Optimisation (dengan Marco Dorigo dan Fred Glover) dan Telekomunikasi Optimisation: heuristik dan Adaptive Teknik (dengan Martin Oates dan George Smith).
Lingkup Pembahasan:
Buku ini memiliki lima bagian utama, dimaksudkan untuk mencakup aspek-aspek utama sistem evolusi yang kreatif. Bagian pertama, "Kreativitas Evolusioner," menjelaskan dan mengeksplorasi hubungan antara kreativitas dan evolusi. Tema yang dibahas dalam bab-bab ini termasuk apa kreativitas (atau tidak), hubungan antara evolusi dan kreativitas, apakah evolusi dapat atau tidak meningkatkan kreativitas seniman, dan isu-isu terkait. Setiap bab mengeksplorasi tema-tema ini dari sudut pandang yang berbeda, mulai dari budaya ke teknis.
Bagian kedua, "Evolusi Musik," memberikan contoh bagaimana sistem evolusi yang digunakan, sering bekerja sama dengan manusia, untuk menghasilkan suara Novel, menyertai, atau komposisi singkat. Salah satu bab membahas tentang kekhawatiran sistem yang menyertai konser jazz live secara real time, dan yang merupakan bintang media musik diterbitkan. Lain dari bab ini di bagian ini menggambarkan sistem evolusi interaktif yang dapat digunakan sebagai bentuk baru alat musik. Bab-bab lain mengeksplorasi konsep evolusi dengan penciptaan bentuk-bentuk baru dari musik dengan cara baru.
Bagian ketiga, "Creative Evolusioner Desain," menyediakan link ke Buku Evolusi Desain oleh Komputer. Bagian ini mengeksplorasi kemampuan evolusi untuk membantu desainer dengan menghasilkan novel dan solusi desain "kreatif". Itu Bab pertama, ditulis oleh seorang arsitek perintis, berpendapat kuat untuk kasus sistem evolusi dalam desain. Bab berikutnya mengeksplorasi subjek menggunakan GP untuk memberikan hasil manusia-kompetitif melalui desain sirkuit analog baru. Ketiga Bab menyelidiki penggunaan sistem Lindenmeyer dengan GP untuk berkembang berguna dan bentuk arsitektur baru. Bab terakhir dari bagian ini menunjukkan bagaimana evolusi dapat digunakan dalam sebuah Alat Cerdas Genetik Desain, memungkinkan desainer untuk mengeksplorasi solusi baru dan kreatif untuk masalah teknik desain.
Bagian Empat Sisi artistik sistem evolusi kreatif, "Seni Evolusioner." Pada bagian ini, Steven Rooke --- salah satu yang paling dikenal saat ini "Seniman evolusi" --- menjelaskan dan menggambarkan bagaimana ia telah menggunakan GP untuk berevolusi seni beberapa potongan menakjubkan. Bab berikutnya, oleh penulis terkenal Artificial Painter, fokus pada seni evolusi apa yang berarti untuk seni dan artis. Bab ketiga, oleh seniman dan penulis Paul Brown, menjelaskan motivasinya sendiri dan inspirasi saat ia menggunakan metode seperti selular automata untuk mengerjakan pekerjaannya. Berikutnya, aplikasi yang sangat praktis untuk metode yang digunakan dalam seni evolusi diperiksa: evolusi kolaboratif wajah fotorealistik. Bab terakhir di bagian ini menjelaskan kolaboratif sistem seni evolusi untuk menghasilkan pekerjaan di gaya Mondrian dan Escher; karya-karya sistem "Escher Evolver" sistem dijelaskan di sini dipamerkan sebagai bagian dari pameran Escher di Museum Kota, Den Haag, pada saat penulisan.
Kelima dan terakhir bagian, "Evolusi Inovasi," meneliti lebih lanjut aplikasi di mana sistem evolusi telah digunakan untuk menghasilkan novel dan hasil yang mengejutkan. Bab sini menggambarkan evolusi jenis baru rangkaian listrik, Novel manuver penerbangan pesawat, antena baru yang aneh untuk diandalkan penerimaan elektromagnetik, terkait kontrol dan morfologi untuk robot dan perangkat fisik lainnya, dan obat-obatan baru dengan aktivitas biologis yang bermanfaat.
Daftar Isi:
Foreword v
By Margaret Boden
Contributors xxiii
Preface xxvii
An Introduction to Creative Evolutionary Systems 1
By PeterJ. Bentley and David W. Corne
Introduction 1
AI and Creativity 2
Evolutionary Computation 4
Creative Evolutionary Systems 36
Is Evolution Creative? 55
PART I Evolutionary Creativity 77
1 Creativity in Evolution" Individuals, Interactions, and Environments 79
By Tim Taylor 79
1 Introduction 79
1.2 Creativity and Opened-Ended Evolution 79
1.3 Design Issues 82
1.3.1 Von Neumann's Architecture for Self-Reproduction 82
1.3.2 Tierra 84
1.3.3 Implicit versus Explicit Encoding 87
1.3.4 Ability to Perform Other Tasks 91
1.3.5 Embeddedness in the Arena of Competition and Richness of Interactions 93
1.3.6 Materiality 98
1.4 A Full Specification For An Open-Ended Evolutionary Process 100
1.4.1 Waddington's Paradigm for an Evolutionary Process 101
1.5 Conclusions 104
2 Recognizability of the Idea" The Evolutionary Process of Argenia 109
By Celestino Soddu
2.1 Introduction 1092.1
2.2 Recognizability, Identity, and Complexity 110
2.3 Evolutionary Codes: Artificial DNA 111
2.4 Natural/Artificial Complexity 112
2.5 Giotto, a Medieval Idea in Evolution 114
2.6 Rome, Future Scenarios 116
2.7 Basilica, Generative Software to Design Complexity 116
2.8 Madrid and Milan, Generated Architecture 119
2.9 Argenia, the Natural Industrial Object, and the Artificial Uniqueness of
Species 121
2.10 Argenic Art: Picasso 123
2.11 Conclusions 125
References 127
3 Breeding Aesthetic Objects" Art and Artificial Evolution 129
By Mitchell Whitelaw
3.1 Introduction 129
3.2 Breeding Aesthetic Objects 130
3.2.1 A Case StudymSteven Rooke 131
3.3 Breeding and Creation 133
3.3.1 Creative Agency and the Breeding Process 134
3.3.2 The Evolved Aesthetic Object 136
3.4 Limits 137
3.5 Driessens and Verstappenman Alternative Approach 139
3.6 Conclusions 144
4 The Beer Can Theory of Creativity 147
By Liane Gabora
4.1 Introduction 147
4.2 Culture as an Evolutionary Process 148
4.2.1 Variation and Convergence in Biology and Culture 148
4.2.2 Is More Than One Mind Necessary for Ideas to Evolve? 150
4.2.3 Meme and Variations: A Computer Model of Cultural Evolution
4.2.4 Breadth-First versus Depth-First Exploration 152
4.2.5 Dampening Arbitrary Associations and Forging Meaningful Ones
4.3 Creativity as the Origin of Culture 154
4.3.1 Theoretical Evidence 155
4.3.2 Archeological Evidence 155
4.3.3 Evidence from Animal Behavior 156
4.4 What Caused the Onset of Creativity? 156
4.5 Conclusions 158
Acknowledgments 159
References 159
PART II Evolutionary Music 163
5 Gen]am" Evolution of a Jazz Improviser 165
By John A. Biles
5.1 Introduction 165
5.2 Overview and Architecture 166
5.3 Representations 168
5.4 Genetic Operators and Training 172
5.4.1 Crossover 173
5.4.2 Musically Meaningful Mutation 175
5.5 Real-Time Interaction 179
5.6 Conclusions 184
References 186
6 On the Origins and Evolution of Music in Virtual Worlds 189
By Eduardo Reck Miranda
6.1 Introduction 189
6.2 Evolutionary Modeling 190
6.2.1 Transformation and Selection 191
6.2.2 Coevolution 192
6.2.3 Self-organization 192
6.2.4 Level Formation 194
6.3 Evolving Sound with Cellular Automata 194
6.3.1 The Basics of Cellular Automata 195
6.3.2 The Cellular Automaton Used in Our System 196
6.3.3 The Synthesis Engine 199
6.4 Commentary on the Results 201
6.5 Conclusions 202
Acknowledgments 202
References 203
7 Vox Populi" Evolutionary Computation for Music Evolution 205
By Artemis Moroni, J6natas Manzolli, Fernando Von Zuben, and Ricardo Gudwin
7.1 Introduction 206
7.2 Sound Attributes 208
7.3 Evolutionary Musical Cycle 208
7.3.1 The Voices Population 209
7.3.2 The Rhythm of the Evolution 210
7.4 Fitness Evaluation 211
7.4.1 The Consonance Criterion 212
7.4.2 Melodic Fitness 214
7.4.3 Harmonic Fitness 214
7.4.4 Voice Range Criterion 215
7.4.5 Musical Fitness 215
7.5 Interface and Parameter Control 216
7.6 Experiments 218
7.7 Conclusions 219
Acknowledgments 220
References 220
8 The Sound Gallery---An Interactive A-Life Artwork 223
By Sam Woolf and Adrian Thompson
8.1 Introduction 223
8.2 Evolvable Hardware 224
8.2.1 ReconfigurableChips 227
8.3 Gallery Setup 228
8.3.1 Setting 228
8.3.2 Sensing Systems 230
8.4 Contextualization: Artificial Life and Art 231
8.4.1 Evolutionary Algorithms and Visual Arts 231
8.4.2 Evolutionary Algorithms and Music 232
8.4.3 Interactive Genetic Art 234
8.4.4 Interactive, Adaptive, and Autonomous (Nongenetic) Artworks 235
8.5 The Sound Gallery Algorithms 237
8.5.1 Two-Phase Hill-Climbing/Island Model GA 238
8.5.2 Hill-Climbing Phase 238
8.5.3 Island Model Genetic Algorithm Phase 239
8.5.4 The Need for Aging 239
8.5.5 Encoding Scheme 240
8.5.6 The Fitness Function 241
8.5.7 galSim 242
8.6 The Experiment 242
8.6.1 Results 244
8.7 Conclusions 247
Acknowledgments 248
References 248
PART III Creative Evolutionary Design 251
9 Creative Design and the Generative Evolutionary Paradigm 253
By John Frazer
9.1 Introduction 253
9.2 The Adaptive Model from Nature 255
9.3 The Generative Evolutionary Paradigm 255
9.4 Problems with the Paradigm 257
9.5 Concept Seeding Approach 259
9.6 The Reptile Demonstration 260
9.7 Universal State Space Modeler 264
9.8 Logic Fields 266
9.9 Returning to the Analogy with Nature 269
9.10 Conclusions 271
References 273
10 Genetic Programming" Biologically Inspired Computation That Exhibits Creativity in
Producing Human-Competitive Results 275
By John R~ Koza, Forrest H. Bennett III, David Andre, and Martin A. Keane
10.1 Introduction 275
10.2 Inventiveness and Creativity 276
10.3 Genetic Programming 280
10.4 Applying Genetic Programming to Circuit Synthesis 283
10.4.1 Campbell 1917 Ladder Filter Patent 285
10.4.2 Zobel 1925 "M-Derived Half Section" Patent 286
10.4.3 Cauer 1934-1936 Elliptic Filter Patents 287
10.4.4 Amplifier, Computational, Temperature-Sensing, Voltage Reference, and
Other Circuits 288
10.5 Topology, Sizing, Placement, and Routing of Circuits 289
10.6 Automatic Synthesis of Controllers by Means of Genetic Programming 290
10.6.1 Robust Controller for a Two-Lag Plant 292
10.7 The Illogical Nature of Creativity and Evolution 294
10.8 Conclusions 296
References 296
11 Toward a Symbiotic Coevolutionary Approach to Architecture 299
By Helen Jackson
11.1 Introduction 299
11.2 Lindemnayer Systems 299
11.2.1 Example L-Systems 300
11.2.2 The Isospatial Grid 302
11.2.3 Spatial Embryology 302
11.3 Artificial Selection 302
11.3.1 The Eyeball Test 304
11.4 Single-Goal Evolution 305
11.4.1 "Generic Function" as Fitness Function 306
11.4.2 Evolution toward Low i-Values 307
11.4.3 Structural Stability 307
11.4.4 Architecture as a Multigoal Task 307
11.4.5 Dual-Goal Evolution 309
11.5 Representation, Systems, and Symbiosis 309
11.5.1 Coevolution 310
11.5.2 Naive Architectural Form Representation 310
11.5.3 Spatial Embryology 311
11.6 Conclusions 311
Acknowledgments 312
References 312
12 Using Evolutionary Algorithms to Aid Designers of Architectural Structures 315
By Peter von Buelow
12.1 Introduction 315
12.2 Analysis Tools vs. Design Tools 316
12.3 Advantages of Evolutionary Systems in Design 317
12.3.1 Use of Populations 317
12.3.2 Recombination and Mutation 318
12.3.3 Wide Search of Design Space 318
12.3.4 No Knowledge of the Objective Function 319
12.3.5 Imitation of Human Design Process 319
12.3.6 Can Learn from Designer 319
12.4 Characteristics o f an IGDT 320
12.4.1 Definition of the IGDT Concept 320
12.4.2 Relation of IGDT to Design Process 322
12.5 Mechanics of an IGDT 323
12.6 IGDT Operation 328
12.6.1 Problem Definition 328
12.6.2 Initial IGDT Generation 329
12.6.3 Initial Generation with Designer Selection/Interaction 330
12.6.4 Second-Generation IGDT Response 331
12.6.5 Second-Generation Designer Interaction 332
12.6.6 Third Generation 332
12.7 Conclusions 335
Acknowledgments 335
References 335
PART IV Evolutionary Art 337
13 Eons of Genetically Evolved Algorithmic Images 339
By Steven Rooke
13.1 Introduction 339
13.2 Using GP for Art 339
13.2.1 Genetic Variation 340
13.2.2 Genetic Library 344
13.2.3 Functions and Node Internals 347
13.2.4 A Typical Run 348
13.3 Horizon Lines and Fantasy Landscapes 351
13.4 Genetic Fractals 351
13.4.1 Second-Order Subtleties of Orbit Trajectories during Iteration in the Complex
Plane 358
13.5 The Genetic Cross Dissolve 358
13.6 What Is It? 360
13.6.1 Constraints of Color and Form 361
13.6.2 A Joyride for the Visual Cortex? 363
13.6.3 Approaching the Organic 364
13.7 Conclusions 364
References 365
14 Art, Robots, and Evolution as a Tool for Creativity 367
By Luigi Pagliarini and Henrik Hautop Lund
14.1 Introduction 367
14.2 The Social Context of Electronics 368
14.2.1 Where Electronics Acts 368
14.2.2 How Technology Influences Art (the world) 369
14.2.3 How Technology Gets Feedback (from Art and the World) 370
14.3 What Artist? 370
14.3.1 Two Different Concepts or Aspects of the Artist 370
14.3.2 Art and Human Language: The "Immaterial" Artist 371
14.3.3 Art and Human Technique: The "Material" Artist 371
14.4 Electronic Art 372
14.4.1 A New Electronic Space 373
14.4.2 The "Material" Electronic Artist 373
14.4.3 The "Immaterial" Artist and the Uses of Electronics 374
14.4.4 Example--The Artificial Painter 375
14.5 Alive Art 379
14.5.1 Other Artistic Movements Based on Electronics 379
14.5.2 Alive Art 380
14.5.3 The Aliver 381
14.5.4 The "Alive Art Effect" 382
14.5.5 Example--LEGO Robot Artists 383
14.6 Conclusions 384
References 384
15 Stepping Stones in the Mist 387
By Paul Brown
15.1 Introduction 387
15.2 On My Approach as an Artist--a Disclaimer 387
15.3 Major Influences 389
15.4 Historical Work--1960s and 1970s 392
15.5 Early Computer Work 396
15.6 Recent Work 402
15.7 Current and Future Directions
15.8 Conclusions 405
Acknowledgments 406
References 407
16 Evolutionary Generation of Faces 409
By PeterJ. B. Hancock and Charlie D. Frowd
16.1 Introduction 409
16.1.1 Eigenfaces 409
16.1.2 Evolutionary Face Generator System 412
16.2 Testing 415
16.2.1 Apparatus 415
16.2.2 Generation of Face Images 415
16.2.3 Evolutionary Algorithm 416
16.2.4 Participants 417
16.3 Results 417
16.4 Discussion 421
16.5 Conclusions 422
Acknowledgments 423
References 423
17 The Escher Evolver: Evolution to the People 425
By A. E. Eiben, K Nabuurs, and I. Booij
17.1 Introduction 425
17.2 The Mathematical System behind Escher's Tiling 427
17.3 Evolutionary Algorithm Design 428
17.3.1 Representation 429
17.3.2 Ground Shape and Transformation System 429
17.3.3 Genetic Operators: Mutation and Crossover 432
17.3.4 Selection Mechanism 433
17.4 Implementation and the Working of the System
17.4.1 Stand-Alone Version 434
17.4.2 First Networked Version 434
17.4.3 Second Networked Version 435
17.5 Conclusions 437
Acknowledgments 439
References 439
PARTV Evolutionary Innovation 441
18 The Genetic Algorithm as a Discovery Engine" Strange Circuits and New Principles 443
By Julian E Mill~, Tatiana Kalganova, DominicJob, and Natalia Lipnitskaya
18.1 Introduction 443
18.2 The Space of All Representations 445
18.3 Evolutionary Algorithms that Assemble Electronic Circuits from a Collection of
Available Components 447
18.3.1 Binary Circuit Symbols 448
18.3.2 Multiple-Valued Circuits 449
18. 4 Results 450
18.4.1 One-Bit Adder 450
18.4.2 Two-Bit Adder 452
18.4.3 Two-Bit Multiplier 454
18.4.4 Three-Bit Multiplier 457
18.4.5 Multiple-Valued One-Digit Adder with Carry 459
18.5 Fingerprinting and Principle Extraction 462
18.6 Conclusions
References 465
19 Discovering Novel Fighter Combat Maneuvers" Simulating Test Pilot Creativity 467
By R. E. Smith, B. A. Dike, B. Ravichandran, A. E1-Fallah, and R. K. Mehra
19.1 Introduction 467
19.2 Fighter Aircraft Maneuvering 469
19.3 Genetics-Based Machine Learning 471
19.3.1 Learning Classifier Systems 472
19.3.2 The LCS Used Here 473
19.4 "One-Sided Learning" Results 479
19.5 "Two-Sided Learning" Results 480
19.6 Differences in Goals and Techniques 382
19.6.1 Implications of This Goal 483
19.7 Conclusions 483
Acknowledgments 485
References 485
20 Innovative Antenna Design Using Genetic Algorithms 487
By Derek S. Linden
20.1 Introduction 487
20.2 Antenna Basics 489
20.3 Conventional Designs and Unconventional Applications: The Yagi-Uda Antenna 493
20.4 Unconventional Designs and Conventional Applications: Crooked-Wire And
Treelike Genetic Antennas 500
20.4.1 The Crooked-Wire Genetic Antenna 501
20.4.2 Treelike Genetic Antennas 506
20.5 Conclusions 509
References 510
21 Evolutionary Techniques in Physical Robotics 551
By Jordan B. Pollack, Hod Lipson, Sevan Ficici, Pablo Funes, Greg Hornby, and Richard A.
Watson
21.1 Introduction 511
21.2 Coevolution 512
21.3 Research Thrusts 513
21.4 Evolution in Simulation 514
21.5 Buildable Simulation 515
21.6 Evolution and Construction of Electromechanical Systems 517
21.7 Embodied Evolution 518
21.8 Conclusions 519
Acknowledgments 520
References 520
22 Patenting Evolved Bactericidal Peptides 525
By Shail Patel, Ian Stott, Manmohan Bhakoo, and Peter Elliott
22.1 Introduction 525
22.2 Design Cycle 526
22.3 Hypothesis: Mechanism of Action 528
33.4 Experimental Measures and Modeling Techniques 529
22.4.1 Molecular Modeling 531
22.4.2 Neural Networks 534
22.5 Evolution 536
22.6 Patent Application 537
22.6.1 Comparing Patent Spaces 539
22.7 Conclusions 542
References 543
Inde Index 547
The color plate section lies between pages 192 and 193.x 547
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