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[资源] Biological Computation - E. Lamm, R. Unger (CRC, 2011)

Preface, xv
Chapter 1 ◾ Introduction and Biological Background 1
1.1 BIOLOGICAL COMPUTATION 1
1.2 THE INFLUENCE OF BIOLOGY ON MATHEMATICS—HISTORICAL EXAMPLES 4
1.3 BIOLOGICAL INTRODUCTION 7
1.3.1 The Cell and Its Activities 12
1.3.2 The Structure of DNA 14
1.3.3 The Genetic Code 16
1.3.4 Protein Synthesis and Gene Regulation 18
1.3.5 Reproduction and Heredity 23
1.4 MODELS AND SIMULATIONS 26
1.5 SUMMARY 33
1.6 FURTHER READING 34
1.7 EXERCISES 34
1.7.1 Biological Computation 34
1.7.2 History 35
1.7.3 Biological Introduction 35
1.7.4 Models and Simulations 37
1.8 ANSWERS TO SELECTED EXERCISES 37
Chapter 2 ◾ Cellular Automata 39
2.1 BIOLOGICAL BACKGROUND 39
viii ◾ Table of Contents
2.1.1 Bacteria Basics 39
2.1.2 Genetic Inheritance—Downward and Sideways 40
2.1.3 Diversity and the Species Question 41
2.1.4 Bacteria and Humans 42
2.1.5 The Sociobiology of Bacteria 42
2.2 THE “GAME OF LIFE” 44
2.3 GENERAL DEFINITION OF CELLULAR AUTOMATA 48
2.4 1-DIMENSIONAL AUTOMATA 50
2.5 EXAMPLES OF CELLULAR AUTOMATA 54
2.5.1 Fur Color 54
2.5.2 Ecological Models 57
2.5.3 Food Chain 58
2.6 COMPARISON WITH A CONTINUOUS MATHEMATICAL MODEL 59
2.7 COMPUTATIONAL UNIVERSALITY 61
2.7.1 What Is Universality? 61
2.7.2 Cellular Automata as a Computational Model 65
2.7.3 How to Prove That a CA Is Universal 67
2.7.4 Universality of a Two-Dimensional Cellular Automaton—Proof Sketch 68
2.7.5 Universality of the “Game of Life”—Proof Sketch 71
2.8 SELF-REPLICATION 73
2.9 SUMMARY 77
2.10 PSEUDO-CODE 78
2.11 FURTHER READING 79
2.12 EXERCISES 79
2.12.1 “Game of Life” 79
2.12.2 Cellular Automata 80
2.12.3 Computing Using Cellular Automata 82
2.12.4 Self-Replication 82
2.12.5 Programming Exercises 83
Table of Contents ◾ ix
2.13 ANSWERS TO SELECTED EXERCISES 84
Chapter 3 ◾ Evolutionary Computation 87
3.1 EVOLUTIONARY BIOLOGY AND EVOLUTIONARY COMPUTATION 87
3.1.1 Natural Selection 87
3.1.2 Evolutionary Computation 93
3.2 GENETIC ALGORITHMS 94
3.2.1 Selection and Fitness 98
3.2.2 Variations on Fitness Functions 102
3.2.3 Genetic Operators and the Representation of Solutions 104
3.3 EXAMPLE APPLICATIONS 108
3.3.1 Scheduling 108
3.3.2 Engineering Optimization 109
3.3.3 Pattern Recognition and Classification 109
3.3.4 Designing Cellular Automata 110
3.3.5 Designing Neural Networks 110
3.3.6 Bioinformatics 110
3.4 ANALYSIS OF THE BEHAVIOR OF GENETIC ALGORITHMS 111
3.4.1 Holland’s Building Blocks Hypothesis 115
3.4.2 The Schema Theorem 116
3.4.3 Corollaries of the Schema Theorem 118
3.5 LAMARCKIAN EVOLUTION 119
3.6 GENETIC PROGRAMMING 121
3.7 A SECOND LOOK AT THE EVOLUTIONARY PROCESS 126
3.7.1 Mechanisms for the Generation and Inheritance of Variations 126
3.7.2 Selection 129
3.8 SUMMARY 130
3.9 PSEUDO-CODE 131
3.10 FURTHER READING 132
x ◾ Table of Contents
3.11 EXERCISES 132
3.11.1 Evolutionary Computation 132
3.11.2 Genetic Algorithms 133
3.11.3 Selection and Fitness 133
3.11.4 Genetic Operators and the Representation of Solutions 134
3.11.5 Analysis of the Behavior of Genetic Algorithms 135
3.11.6 Genetic Programming 136
3.11.7 Programming Exercises 136
3.12 ANSWERS TO SELECTED EXERCISES 140
Chapter 4 ◾ Artificial Neural Networks 143
4.1 BIOLOGICAL BACKGROUND 143
4.1.1 Neural Networks as Computational Model 146
4.2 LEARNING 146
4.3 ARTIFICIAL NEURAL NETWORKS 148
4.3.1 General Structure of Artificial Neural Networks 148
4.3.2 Training an Artificial Neural Network 151
4.4 THE PERCEPTRON 152
4.4.1 Definition of a Perceptron 152
4.4.2 Formal Description of the Behavior of a Perceptron 156
4.4.3 The Perceptron Learning Rule 158
4.4.4 Proving the Convergence of the Perceptron Learning Algorithm 159
4.5 LEARNING IN A MULTILAYERED NETWORK 162
4.5.1 The Backpropagation Algorithm 162
4.5.2 Analysis of Learning Algorithms 170
4.5.3 Network Design 172
4.5.4 Examples of Applications 174
4.6 ASSOCIATIVE MEMORY 180
4.6.1 Biological Memory 180
4.6.2 Hopfield Networks 181
Table of Contents ◾ xi
4.6.3 Memorization in a Hopfield Network 181
4.6.4 Data Retrieval in a Hopfield Network 183
4.6.5 The Convergence of the Process of Updating the Neurons 185
4.6.6 Analyzing the Capacity of a Hopfield Network 186
4.6.7 Application of a Hopfield Network 189
4.6.8 Further Uses of the Hopfield Network 191
4.7 UNSUPERVISED LEARNING 194
4.7.1 Self-Organizing Maps 195
4.7.2 WEBSOM: Example of Using SOMs for Document Text Mining 198
4.8 SUMMARY 200
4.9 FURTHER READING 201
4.10 EXERCISES 202
4.10.1 Single-Layer Perceptrons 202
4.10.2 Multilayer Networks 203
4.10.3 Hopfield Networks 205
4.10.4 Self-Organizing Maps 208
4.10.5 Summary 208
4.11 ANSWERS TO SELECTED EXERCISES 210
Chapter 5 ◾ Molecular Computation 215
5.1 BIOLOGICAL BACKGROUND 217
5.1.1 PCR: Polymerase Chain Reaction 217
5.1.2 Gel Electrophoresis 219
5.1.3 Restriction Enzymes 219
5.1.4 Ligation 220
5.2 COMPUTATION USING DNA 220
5.2.1 Hamiltonian Paths 220
5.2.2 Solving SAT 230
5.2.3 DNA Tiling 233
5.2.4 DNA Computing—Summary 236
xii ◾ Table of Contents
5.3 ENZYMATIC COMPUTATION 237
5.3.1 Finite Automata 238
5.3.2 Enzymatic Implementation of Finite Automata 242
5.4 SUMMARY 248
5.5 FURTHER READING 250
5.6 EXERCISES 250
5.6.1 Biological Background 250
5.6.2 Computing with DNA 250
5.6.3 Enzymatic Computation 253
5.7 ANSWERS TO SELECTED EXERCISES 254
Chapter 6 ◾ The Never-Ending Story: Additional Topics at the Interface between Biology and Computation 259
6.1 SWARM INTELLIGENCE 261
6.1.1 Ant Colony Optimization Algorithms 262
6.1.2 Cemetery Organization, Larval Sorting, and Clustering 264
6.1.3 Particle Swarm Optimization 267
6.2 ARTIFICIAL IMMUNE SYSTEMS 270
6.2.1 Identifying Intrusions in a Computer Network 271
6.3 ARTIFICIAL LIFE 273
6.3.1 Avida 276
6.3.2 Evolvable Virtual Creatures 281
6.4 SYSTEMS BIOLOGY 284
6.4.1 Evolution of Modularity 287
6.4.2 Robustness of Biological Systems 289
6.4.3 Formal Languages for Describing Biological Systems 290
6.5 SUMMARY 294
6.6 RECOMMENDATIONS FOR ADDITIONAL READING 297
6.6.1 Biological Introduction 297
Table of Contents ◾ xiii
6.6.2 Personal Perspectives 298
6.6.3 Modeling Biological Systems 298
6.6.4 Biological Computation 299
6.6.5 Cellular Automata 299
6.6.6 Evolutionary Computation 300
6.6.7 Neural Networks 300
6.6.8 Molecular Computation 300
6.6.9 Swarm Intelligence 300
6.6.10 Systems Biology 301
6.6.11 Bioinformatics 301
6.7 FURTHER READING 301
6.8 EXERCISES 302
6.8.1 Swarm Intelligence 302
6.8.2 Artificial Immune Systems 303
6.8.3 Artificial Life 305
6.8.4 Systems Biology 306
6.8.5 Programming Exercises 306
6.9 ANSWERS TO SELECTED EXERCISES 307

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