Preface |
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xi | |
Acknowledgments |
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xiii | |
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1 | (100) |
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1.1 Pillars of Evolutionary Theory |
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2 | (3) |
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5 | (8) |
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13 | (3) |
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1.4 Genetic Representations |
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16 | (5) |
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21 | (1) |
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22 | (1) |
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1.7 Selection and Reproduction |
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23 | (3) |
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26 | (3) |
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1.9 Evolutionary Measures |
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29 | (4) |
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1.10 Types of Evolutionary Algorithms |
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33 | (4) |
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37 | (2) |
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1.12 Human-Competitive Evolution |
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39 | (3) |
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1.13 Evolutionary Electronics |
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42 | (1) |
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1.14 Lessons from Evolutionary Electronics |
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43 | (2) |
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1.15 The Role of Abstraction |
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45 | (4) |
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1.16 Analog and Digital Circuits |
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49 | (4) |
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1.17 Extrinsic and Intrinsic Evolution |
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53 | (5) |
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58 | (4) |
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1.19 Evolutionary Digital Design |
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62 | (15) |
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77 | (2) |
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1.21 Evolutionary Analog Design |
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79 | (6) |
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1.22 Multiple Objectives and Constraints |
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85 | (5) |
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90 | (2) |
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92 | (5) |
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97 | (4) |
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101 | (62) |
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2.1 The Basic Ingredients |
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101 | (6) |
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107 | (3) |
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2.3 Modeling with Cellular Systems |
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110 | (8) |
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2.4 Some Classic Cellular Automata |
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118 | (6) |
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2.5 Other Cellular Systems |
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124 | (10) |
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134 | (4) |
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138 | (7) |
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145 | (8) |
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2.9 Analysis and Synthesis of Cellular Systems |
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153 | (6) |
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159 | (1) |
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160 | (3) |
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163 | (106) |
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3.1 Biological Nervous Systems |
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167 | (8) |
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3.2 Artificial Neural Networks |
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175 | (2) |
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177 | (12) |
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189 | (2) |
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191 | (5) |
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196 | (2) |
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3.7 Unsupervised Learning |
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198 | (21) |
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219 | (16) |
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3.9 Reinforcement Learning |
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235 | (3) |
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3.10 Evolution of Neural Networks |
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238 | (12) |
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250 | (6) |
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3.12 Hybrid Neural Systems |
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256 | (5) |
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261 | (4) |
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265 | (4) |
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269 | (66) |
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4.1 Potential Advantages of a Developmental Representation |
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270 | (2) |
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272 | (24) |
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4.3 Synthesis of Developmental Systems |
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296 | (2) |
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4.4 Evolution and Development |
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298 | (1) |
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4.5 Defining Artificial Evolutionary Developmental Systems |
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299 | (2) |
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4.6 Evolutionary Rewriting Systems |
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301 | (9) |
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4.7 Evolutionary Developmental Programs |
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310 | (5) |
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4.8 Evolutionary Developmental Processes |
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315 | (17) |
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332 | (2) |
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334 | (1) |
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335 | (64) |
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5.1 How Biological Immune Systems Work |
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337 | (16) |
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5.2 The Constituents of Biological Immune Systems |
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353 | (13) |
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5.3 Lessons for Artificial Immune Systems |
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366 | (7) |
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5.4 Algorithms and Applications |
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373 | (2) |
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375 | (9) |
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5.6 Negative Selection Algorithm |
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384 | (4) |
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5.7 Clonal Selection Algorithm |
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388 | (2) |
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390 | (5) |
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395 | (1) |
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396 | (3) |
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399 | (116) |
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6.1 Behavior in Cognitive Science |
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400 | (3) |
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6.2 Behavior in Artificial Intelligence |
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403 | (4) |
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6.3 Behavior-Based Robotics |
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407 | (12) |
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6.4 Biological Inspiration for Robots |
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419 | (18) |
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6.5 Robots as Biological Models |
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437 | (12) |
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449 | (11) |
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6.7 Evolution of Behavioral Systems |
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460 | (22) |
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6.8 Evolution and Learning in Behavioral Systems |
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482 | (12) |
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6.9 Evolution and Neural Development in Behavioral Systems |
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494 | (5) |
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6.10 Coevolution of Body and Control |
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499 | (5) |
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6.11 Toward Self-Reproduction |
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504 | (3) |
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6.12 Simulation and Reality |
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507 | (4) |
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511 | (2) |
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513 | (2) |
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515 | (70) |
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7.1 Biological Self-Organization |
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516 | (8) |
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7.2 Particle Swarm Optimization |
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524 | (3) |
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7.3 Ant Colony Optimization |
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527 | (4) |
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531 | (16) |
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7.5 Coevolutionary Dynamics: Biological Models |
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547 | (7) |
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7.6 Artificial Evolution of Competing Systems |
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554 | (18) |
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7.7 Artificial Evolution of Cooperation |
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572 | (9) |
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581 | (2) |
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583 | (2) |
Conclusion |
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585 | (2) |
References |
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587 | (64) |
Index |
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651 | |