Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications,9781584886433

Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications

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Edition: 1st
ISBN13: 9781584886433
ISBN10: 1584886439
Format: Hardcover
Pub. Date: 2006-06-02
Publisher(s): Chapman & Hall/
List Price: $120.00

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Summary

Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniques and important applications of nature-based computing.This book presents theoretical and philosophical discussions, pseudocodes for algorithms, and computing paradigms that illustrate how computational techniques can be used to solve complex problems, simulate nature, explain natural phenomena, and possibly allow the development of new computing technologies. The author features a consistent and approachable, textbook-style format that includes lucid figures, tables, real-world examples, and different types of exercises that complement the concepts while encouraging readers to apply the computational tools in each chapter. Building progressively upon core concepts of nature-inspired techniques, the topics include evolutionary computing, neurocomputing, swarm intelligence, immunocomputing, fractal geometry, artificial life, quantum computing, and DNA computing.Fundamentals of Natural Computing is a self-contained introduction and a practical guide to nature-based computational approaches that will find numerous applications in a variety of growing fields including engineering, computer science, biological modeling, and bioinformatics.

Table of Contents

1. FROM NATURE TO NATURAL COMPUTING
1.1 INTRODUCTION
1(2)
1.1.1. MOTIVATION
2(1)
1.2 A SMALL SAMPLE OF IDEAS
3(4)
1.3 THE PHILOSOPHY OF NATURAL COMPUTING
7(1)
1.4 THE THREE BRANCHES: A BRIEF OVERVIEW
8(7)
1.4.1. COMPUTING INSPIRED BY NATURE
8(3)
1.4.2. THE SIMULATION AND EMULATION OF NATURE IN COMPUTERS
11(2)
1.4.3. COMPUTING WITH NATURAL MATERIALS
13(2)
1.5 WHEN TO USE NATURAL COMPUTING APPROACHES
15(4)
1.6 SUMMARY
19(1)
1.7 QUESTIONS
19(1)
1.8 REFERENCES
20(5)
2. CONCEPTUALIZATION
2.1 INTRODUCTION
25(6)
2.1.1. NATURAL PHENOMENA, MODELS, AND METAPHORS
26(3)
2.1.2. FROM NATURE TO COMPUTING AND BACK AGAIN
29(2)
2.2 GENERAL CONCEPTS
31(22)
2.2.1. INDIVIDUALS, ENTITIES, AND AGENTS
31(1)
2.2.2. PARALLELISM AND DISTRIBUTIVITY
32(1)
2.2.3. INTERACTIVITY
33(3)
Connectivity
34(1)
Stigmergy
35(1)
2.2.4. ADAPTATION
36(2)
Learning
36(1)
Evolution
37(1)
2.2.5. FEEDBACK
38(2)
Positive Feedback
38(1)
Negative Feedback
39(1)
2.2.6. SELF-ORGANIZATION
40(4)
Characteristics of Self-Organization
43(1)
Alternatives to Self-Organization
43(1)
2.2.7. COMPLEXITY, EMERGENCE, AND REDUCTIONISM
44(5)
Complexity
44(2)
Emergence
46(2)
Reductionism
48(1)
2.2.8. BOTTOM-UP VS. TOP-DOWN
49(2)
Bottom-Up
49(1)
Top-Down
50(1)
2.2.9. DETERMINISM, CHAOS, AND FRACTALS
51(2)
2.3 SUMMARY
53(1)
2.4 EXERCISES
53(2)
2.4.1. QUESTIONS
53(1)
2.4.2. THOUGHT EXERCISE
54(1)
2.4.3. PROJECTS AND CHALLENGES
54(1)
2.5 REFERENCES
55(6)
PART I COMPUTING INSPIRED BY NATURE
3. EVOLUTIONARY COMPUTING
3.1 INTRODUCTION
61(1)
3.2 PROBLEM SOLVING AS A SEARCH TASK
62(3)
3.2.1. DEFINING A SEARCH PROBLEM
63(2)
3.3 HILL CLIMBING AND SIMULATED ANNEALING
65(8)
3.3.1. HILL CLIMBING
65(3)
3.3.2. SIMULATED ANNEALING
68(4)
Basic Principles of Statistical Thermodynamics
69(1)
The Simulated Annealing Algorithm
69(2)
From Statistical Thermodynamics to Computing
71(1)
3.3.3. EXAMPLE OF APPLICATION
72(1)
3.4 EVOLUTIONARY BIOLOGY
73(13)
3.4.1. ON THE THEORY OF EVOLUTION
74(1)
3.4.2. DARWIN'S DANGEROUS IDEA
75(1)
3.4.3. BASIC PRINCIPLES OF GENETICS
76(6)
3.4.4. EVOLUTION AS AN OUTCOME OF GENETIC VARIATION PLUS SELECTION
82(2)
3.4.5. A CLASSIC EXAMPLE OF EVOLUTION
84(1)
3.4.6. A SUMMARY OF EVOLUTIONARY BIOLOGY
85(1)
3.5 EVOLUTIONARY COMPUTING
86(14)
3.5.1. STANDARD EVOLUTIONARY ALGORITHM
86(2)
3.5.2. GENETIC ALGORITHMS
88(3)
Roulette Wheel Selection
89(1)
Crossover
90(1)
Mutation
91(1)
3.5.3. EXAMPLES OF APPLICATION
91(8)
A Step by Step Example: Pattern Recognition (Learning)
92(6)
Numerical Function Optimization
98(1)
3.5.4. HILL-CLIMBING, SIMULATED ANNEALING, AND GENETIC ALGORITHMS
99(1)
3.6 THE OTHER MAIN EVOLUTIONARY ALGORITHMS
100(12)
3.6.1. EVOLUTION STRATEGIES
100(3)
Selection
101(1)
Crossover
101(1)
Mutation
102(1)
3.6.2. EVOLUTIONARY PROGRAMMING
103(2)
Selection
104(1)
Mutation
104(1)
3.6.3. GENETIC PROGRAMMING
105(3)
Crossover
107(1)
Mutation
107(1)
3.6.4. SELECTED APPLICATIONS FROM THE LITERATURE: A BRIEF DESCRIPTION
108(6)
ES: Engineering Design
108(2)
EP: Parameter Optimization
110(1)
GP: Pattern Classification
111(1)
3.7 FROM EVOLUTIONARY BIOLOGY TO COMPUTING
112(1)
3.8 SCOPE OF EVOLUTIONARY COMPUTING
113(1)
3.9 SUMMARY
114(1)
3.9.1. THE BLIND WATCHMAKER
114(1)
3.10 EXERCISES
115(4)
3.10.1. QUESTIONS
115(1)
3.10.2. COMPUTATIONAL EXERCISES
116(2)
3.10.3. THOUGHT EXERCISES
118(1)
3.10.4. PROJECTS AND CHALLENGES
118(1)
3.11 REFERENCES
119(4)
4. NEUROCOMPUTING
4.1 INTRODUCTION
123(1)
4.2 THE NERVOUS SYSTEM
124(8)
4.2.1. LEVELS OF ORGANIZATION IN THE NERVOUS SYSTEM
126(5)
Neurons and Synapses
126(3)
Networks, Layers, and Maps
129(2)
4.2.2. BIOLOGICAL AND PHYSICAL BASIS OF LEARNING AND MEMORY
131(1)
4.3 ARTIFICIAL NEURAL NETWORKS
132(19)
4.3.1. ARTIFICIAL NEURONS
133(7)
The McCulloch and Pitts Neuron
134(1)
A Basic Integrate-and-Fire Neuron
135(1)
The Generic Neurocomputing Neuron
136(4)
4.3.2 NETWORK ARCHITECTURES
140(5)
Single-Layer Feedforward Networks
141(1)
Multi-Layer Feedforward Networks
142(2)
Recurrent Networks
144(1)
4.3.3 LEARNING APPROACHES
145(6)
Supervised Learning
146(2)
Unsupervised Learning
148(2)
Reinforcement Learning
150(1)
4.4 TYPICAL ANNS AND LEARNING ALGORITHMS
151(42)
4.4.1 HEBBIAN LEARNING
152(1)
Biological Basis of Hebbian Synaptic Modification
153(1)
4.4.2 SINGLE-LAYER PERCEPTRON
153(7)
Linear Separability
153(1)
Simple Perceptron for Pattern Classification
154(2)
Multiple Output Perceptron for Pattern Classification
156(1)
Examples of Application
157(3)
4.4.3 ADALINE, THE LMS ALGORITHM, AND ERROR SURFACES
160(3)
LMS Algorithm (Delta Rule)
161(1)
Error Surfaces
162(1)
4.4.4 MULTI-LAYER PERCEPTRON
163(15)
The Backpropagation Learning Algorithm
164(6)
Universal Function Approximation
170(2)
Some Practical Aspects
172(1)
Biological Plausibility of Backpropagation
173(1)
Examples of Application
174(4)
4.4.5 SELF-ORGANIZING MAPS
178(10)
Self-Organizing Map Learning Algorithm
180(5)
Biological Basis and Inspiration for the Self-Organizing Map
185(1)
Examples of Applications
186(2)
4.4.6 DISCRETE HOPFIELD NETWORK
188(7)
Recurrent Neural Networks as Nonlinear Dynamical Systems
189(2)
Discrete Hopfield Network
191(1)
Spurious Attractors
192(1)
Example of Application
193(1)
4.5 FROM NATURAL TO ARTIFICIAL NEURAL NETWORKS
193(1)
4.6 SCOPE OF NEUROCOMPUTING
194(1)
4.7 SUMMARY
195(1)
4.8 EXERCISES
195(7)
4.8.1 QUESTIONS
195(1)
4.8.2 COMPUTATIONAL EXERCISES
196(5)
4.8.3 THOUGHT EXERCISES
201(1)
4.8.4 PROJECTS AND CHALLENGES
201(1)
4.9 REFERENCES
202(3)
5. SWARM INTELLIGENCE
5.1. INTRODUCTION
205(2)
5.2. ANT COLONIES
207(28)
5.2.1 ANTS AT WORK: How AN INSECT SOCIETY IS ORGANIZED
208(2)
5.2.2 ANT FORAGING BEHAVIOR
210(503)
Stigmergy
213(500)
5.2.3 ANT COLONY OPTIMIZATION (ACO)
713
The Simple Ant Colony Optimization Algorithm (S-ACO)
215(2)
General-Purpose Ant Colony Optimization Algorithm
217(1)
Selected Applications from the Literature: A Brief Description
218(4)
Scope of ACO Algorithms
222(1)
From Natural to Artificial Ants
223(1)
5.2.4 CLUSTERING OF DEAD BODIES AND LARVAL SORTING IN ANT COLONIES
224(1)
Stigmergy
225(1)
5.2.5 ANT CLUSTERING ALGORI IM (ACA)
225(9)
The Standard Ant Clustering Algorithm (ACA)
226(2)
Selected Applications from the Literature: A Brief Description
228(505)
Scope of Ant Clustering Algorithms
733(1)
From Natural to Artificial Ants
733
5.2.6 SUMMARY OF SWARM SYSTEMS BASED ON SOCIAL INSECTS
234(1)
5.3. SWARM ROBOTICS
235(11)
5.3.1 FORAGING FOR FOOD
5.3.2 CLUSTERING OE OBJECTS
238(3)
5.3.3 COLLECTIVE PREY RETRIEVAL
241(4)
Cooperative Box Pushing
241(2)
Recruitment of Nestmates
243(2)
5.3.4 SCOPE OF SWARM ROBOTICS
245(1)
5.3.5 SUMMARY OF SWARM ROBOTICS
245(1)
5.4. SOCIAL ADAPTATION OF KNOWLEDGE
246(10)
5.4.1 PARTICLE SWARM
247(4)
5.4.2 SELECTED APPLICATIONS FROM THE LITERATURE: A BRIEF DESCRIPTION
251(3)
Optimization of Neural Network Weights
251(2)
Numerical Function Optimization
253(1)
5.4.3 SCOPE OF PARTICLE SWARM OPTIMIZATION
254(1)
5.4.4 FROM SOCIAL SYSTEMS TO PARTICLE SWARM
255(1)
5.4.5 SUMMARY OF PARTICLE SWARM OPTIMIZATION
255(1)
5.5. SUMMARY
256(1)
5.6. EXERCISES
257(5)
5.6.1 QUESTIONS
257(2)
5.6.2 COMPUTATIONAL EXERCISES
259(2)
5.6.3 THOUGHT EXERCISES
261(1)
5.6.4 PROJECTS AND CHALLENGES
261(1)
5.7. REFERENCES
262(5)
6. IMMUNOCOMPUTING
6.1 INTRODUCTION
267(1)
6.2 THE IMMUNE SYSTEM
268(12)
6.2.1. PHYSIOLOGY AND MAIN COMPONENTS
270(1)
6.2.2. PATTERN RECOGNITION AND BINDING
271(1)
6.2.3. ADAPTIVE IMMUNE RESPONSE
272(4)
Adaptation via Clonal Selection
274(1)
Clonal Selection and Darwinian Evolution
275(1)
6.2.4. SELF/NONSELF DISCRIMINATION
276(1)
6.2.5. THE IMMUNE NETWORK THEORY
276(2)
Adaptation and Learning via Immune Network
277(1)
6.2.6. DANGER THEORY
278(1)
6.2.7. A BROADER PICTURE
279(1)
6.3 ARTIFICIAL IMMUNE SYSTEMS
280(5)
6.3.1. REPRESENTATION
281(1)
6.3.2. EVALUATING INTERACTIONS
282(2)
6.3.3. IMMUNE ALGORITHMS
284(1)
6.4 BONE MARROW MODELS
285(3)
6.4.1. SELECTED APPLICATIONS FROM THE LITERATURE: A BRIEF DESCRIPTION
286(2)
Evolution of the Genetic Encoding of Antibodies
287(1)
Antigenic Coverage and Evolution of Antibody Gene Libraries
287(1)
Generating Antibodies for Job Shop Scheduling
288(1)
6.5 NEGATIVE SELECTION ALGORITHMS
288(8)
6.5.1. BINARY NEGATIVE SELECTION ALGORITHM
289(2)
6.5.2. REAL-VALUED NEGATIVE SELECTION ALGORITHM
291(2)
6.5.3. SELECTED APPLICATIONS FROM THE LITERATURE: A BRIEF DESCRIPTION
293(3)
Network Intrusion Detection
293(2)
Breast Cancer Diagnosis
295(1)
6.6 CLONAL SELECTION AND AFFINITY MATURATION
296(7)
6.6.1. FORREST'S ALGORITHM
297(1)
6.6.2. CLONALG
298(3)
6.6.3. SELECTED APPLICATIONS FROM THE LITERATURE: A BRIEF DESCRIPTION
301(2)
Pattern Recognition
301(2)
Multimodal Function Optimization
303(1)
6.7 ARTIFICIAL IMMUNE NETWORKS
303(9)
6.7.1. CONTINUOUS IMMUNE NETWORKS
304(2)
6.7.2. DISCRETE IMMUNE NETWORKS
306(3)
6.7.3. SELECTED APPLICATIONS FROM THE LITERATURE: A BRIEF DESCRIPTION
309(5)
A Recommender System
309(1)
Data Compression and Clustering
310(2)
6.8 FROM NATURAL TO ARTIFICIAL IMMUNE SYSTEMS
312(1)
6.9 SCOPE OF ARTIFICIAL IMMUNE SYSTEMS
313(1)
6.10 SUMMARY
313(1)
6.11 EXERCISES
314(6)
6.11.1. QUESTIONS
314(1)
6.11.2. COMPUTATIONAL EXERCISES
314(4)
6.11.3. THOUGHT EXERCISES
318(1)
6.11.4. PROJECTS AND CHALLENGES
319(1)
6.12 REFERENCES
320(7)
PART II THE SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS
7. FRACTAL GEOMETRY OF NATURE
7.1 INTRODUCTION
327(1)
7.2 THE FRACTAL GEOMETRY OF NATURE
328(12)
7.2.1. SELF-SIMILARITY
329(2)
7.2.2. SOME PIONEERING FRACTALS
331(3)
7.2.3. DIMENSION AND FRACTAL DIMENSION
334(5)
7.2.4. SCOPE OF FRACTAL GEOMETRY
339(1)
7.3 CELLULAR AUTOMATA
340(7)
7.3.1. A SIMPLE ONE-DIMENSIONAL EXAMPLE
341(1)
7.3.2. CELLULAR AUTOMATA AS DYNAMICAL SYSTEMS
342(2)
7.3.3. FORMAL DEFINITION
344(1)
7.3.4. EXAMPLE OF APPLICATION
345(2)
Fractal Patterns
345(2)
7.3.5. SCOPE OF CELLULAR AUTOMATA
347(1)
7.4 L-SYSTEMS
347(8)
7.4.1. DOL-SYSTEMS
348(2)
7.4.2. TURTLE GRAPHICS
350(2)
7.4.3. MODELS OF PLANT ARCHITECTURE
352(3)
7.4.4. SCOPE OF L-SYSTEMS
355(1)
7.5 ITERATED FUNCTION SYSTEMS
355(7)
7.5.1. ITERATED FUNCTION SYSTEMS (IFS)
355(4)
Deterministic Iterated Function System (DIFS)
356(1)
Random Iterated Function System (RIFS)
357(2)
7.5.2. CREATING FRACTALS WITH IFS
359(1)
7.5.3. SELF-SIMILARITY REVISITED
359(3)
7.5.4. SCOPE OF IFS
362(1)
7.6 FRACTIONAL BROWNIAN MOTION
362(9)
7.6.1. RANDOM FRACTALS IN NATURE AND BROWNIAN MOTION
362(5)
7.6.2. FRACTIONAL BROWNIAN MOTION
367(3)
7.6.3. SCOPE OF FBM
370(1)
7.7 PARTICLE SYSTEMS
371(6)
7.7.1. PRINCIPLES OF PARTICLE SYSTEMS
371(1)
7.7.2. BASIC MODEL OF PARTICLE SYSTEMS
372(2)
Particle Generation
373(1)
Particle Attributes
373(1)
Particle Extinction
374(1)
Particle Dynamics
374(1)
Particle Rendering
374(1)
7.7.3. PSEUDOCODE AND EXAMPLES
374(3)
7.7.4. SCOPE OF PARTICLE SYSTEMS
377(1)
7.8 EVOLVING THE GEOMETRY OF NATURE
377(3)
7.8.1. EVOLVING PLANT-LIKE STRUCTURES
377(3)
7.8.2. SCOPE OF EVOLUTIONARY GEOMETRY
380(1)
7.9 FROM NATURAL TO FRACTAL GEOMETRY
380(2)
7.10 SUMMARY
382(1)
7.11 EXERCISES
382(6)
7.11.1. QUESTIONS
382(1)
7.11.2. COMPUTATIONAL EXERCISES
383(3)
7.11.3. THOUGHT EXERCISES
386(1)
7.11.4. PROJECTS AND CHALLENGES
386(2)
7.12 REFERENCES
388(3)
8. ARTIFICIAL LIFE
8.1 INTRODUCTION
391(3)
8.1.1. A DISCUSSION ABOUT THE STRUCTURE OF THE CHAPTER
393(1)
8.2 CONCEPTS AND FEATURES OF ARTIFICIAL LIFE SYSTEMS
394(5)
8.2.1. ARTIFICIAL LIFE AND COMPUTING INSPIRED BY NATURE
394(1)
8.2.2. LIFE AND ARTIFICIAL ORGANISMS
394(2)
8.2.3. ARTIFICIAL LIFE AND BIOLOGY
396(1)
8.2.4. MODELS AND FEATURES OF COMPUTER-BASED ALIFE
397(1)
8.2.5. ALIFE SYSTEMS AS COMPLEX (ADAPTIVE) SYSTEMS
398(1)
8.3 EXAMPLES OF ARTIFICIAL LIFE PROJECTS
399(39)
8.3.1. FLOCKS, HERDS, AND SCHOOLS
399(3)
Discussion and Applications
401(1)
8.3.2. BIOMORPHS
402(4)
Discussion and Applications
405(1)
8.3.3. COMPUTER VIRUSES
406(2)
Discussion and Applications
408(1)
8.3.4. SYNTHESIZING EMOTIONAL BEHAVIOR
408(2)
Discussion and Applications
410(1)
8.3.5. AEBO ROBOT
410(3)
Discussion and Applications
412(1)
8.3.6. WASP NEST BUILDING
413(3)
Discussion and Applications
415(1)
8.3.7. CREATURES
416(3)
Discussion and Applications
419(1)
8.3.8. ARTIFICIAL FISHES
419(2)
Discussion and Applications
421(1)
8.3.9. TURTLES, TERMITES, AND TRAFFIC JAMS
421(7)
Predator-Prey Interactions
422(2)
Termites
424(1)
Traffic Jams
425(1)
Slime-Mold
426(2)
Discussion and Applications
428(1)
8.3.10. CELLULAR AUTOMATA SIMULATIONS
428(6)
The Game of Life
428(3)
Langton's Loops
431(1)
CAFUN
432(2)
8.3.11. FRAMSTICKS
434(6)
Architecture of the Framsticks and Its Environment
435(2)
Evolving the Framsticks
437(1)
Discussion and Applications
437(1)
8.4 SCOPE OF ARTIFICIAL LIFE
438(1)
8.5 FROM ARTIFICIAL LIFE TO LIFE-AS-WE-KNOW-IT
438(1)
8.6 SUMMARY
439(1)
8.7 EXERCISES
440(3)
8.7.1. QUESTIONS
440(2)
8.7.2. COMPUTATIONAL EXERCISES
442(1)
8.7.3. THOUGHT EXERCISE
442(1)
8.7.4. PROJECTS AND CHALLENGES
442(1)
8.8 REFERENCES
443(6)
PART III COMPUTING WITH NEW NATURAL MATERIALS
9. DNA COMPUTING
9.1 INTRODUCTION
449(2)
9.1.1. MOTIVATION
451(1)
9.2 BASIC CONCEPTS FROM MOLECULAR BIOLOGY
451(11)
9.2.1. THE DNA MOLECULE
451(5)
9.2.2. MANIPULATING DNA
456(6)
9.3 FILTERING MODELS
462(11)
9.3.1. ADLEMAN'S EXPERIMENT
462(4)
Discussion
465(1)
9.3.2. LIPTON'S SOLUTION TO THE SAT PROBLEM
466(2)
Discussion
468(1)
9.3.3. TEST TUBE PROGRAMMING LANGUAGE
469(1)
The Unrestricted DNA Model
469(1)
Examples of Application
470(2)
An Extension of the Unrestricted DNA Model
472(1)
The DNA Pascal
472(1)
9.4 FORMAL MODELS: A BRIEF DESCRIPTION
473(3)
9.4.1. STICKER SYSTEMS
474(1)
9.4.2. SPLICING SYSTEMS
474(1)
9.4.3. INSERTION/DELETION SYSTEMS
475(1)
9.4.4. THE PAM MODEL
475(1)
9.5 UNIVERSAL DNA COMPUTERS
476(3)
9.6 SCOPE OF DNA COMPUTING
479(1)
9.7 FROM CLASSICAL TO DNA COMPUTING
480(1)
9.8 SUMMARY AND DISCUSSION
480(2)
9.9 EXERCISES
482(2)
9.9.1. QUESTIONS
482(1)
9.9.2. COMPUTATIONAL EXERCISES
482(1)
9.9.3. THOUGHT EXERCISES
483(1)
9.9.4. PROJECTS AND CHALLENGES
483(1)
9.10 REFERENCES
484(3)
10. QUANTUM COMPUTING
10.1 INTRODUCTION
487(2)
10.1.1. MOTIVATION
488(1)
10.2 BASIC CONCEPTS FROM QUANTUM THEORY
489(6)
10.2.1. FROM CLASSICAL TO QUANTUM MECHANICS
489(1)
10.2.2. WAVE-PARTICLE DUALITY
490(4)
Double-Slit with Bullets
491(1)
Double-Slit with Water Waves
491(1)
Double-Slit with Electrons
492(2)
10.2.3. THE UNCERTAINTY PRINCIPLF
494(1)
10.2.4. SOME REMARKS
495(1)
10.3 PRINCIPLES FROM QUANTUM MECHANICS
495(6)
10.3.1. DIRAC NOTATION
495(1)
10.3.2. QUANTUM SUPERPOSITION
496(1)
10.3.3. TENSOR PRODUCTS
497(1)
10.3.4. ENTANGLEMENT
497(1)
10.3.5. EVOLUTION (DYNAMICS)
498(1)
10.3.6. MEASUREMENT
499(1)
10.3.7. NO-CLONING THEOREM
500(1)
10.4 QUANTUM INFORMATION
501(14)
10.4.1. BITS AND QUANTUM BITS (QUBITS)
501(2)
10.4.2. MULTIPLE: BBITS AND QUBITS
503(1)
10.4.3. GATES AND QUANTUM GATES
503(5)
Generalizations of the Hadamard Gate
507(1)
10.4.4. QUANTUM CIRCUITS
508(2)
10.4.5. QUANTUM PARALLELISM
510(1)
10.4.6. EXAMPLES OF APPLICATIONS
511(4)
Dense Coding
511(2)
Quantum Teleportation
513(2)
10.5 UNIVERSAL QUANTUM COMPUTERS
515(3)
10.5.1. BENIOFF'S COMPUTER
515(1)
10.5.2. FEYNMAN'S COMPUTER
516(1)
10.5.3. DEUTSCH'S COMPUTER
517(1)
10.6 QUANTUM ALGORITHMS
518(8)
10.6.1. DEUTSCH-JOZSA ALGORITHM
519(2)
10.6.2. SIMON'S ALGORITHM
521(1)
10.6.3. SHOR'S ALGORITHM
522(3)
Quantum Fourier Transform
522(1)
Factorization
523(2)
10.6.4. GROVER'S ALGORITHM
525(1)
10.7 PHYSICAL REALIZATIONS OF QUANTUM COMPUTERS: A BRIEF DESCRIPTION
526(2)
10.7.1. ION TRAPS
527(1)
10.7.2. CAVITY QUANTUM ELECTRODYNAMICS (CQED)
527(1)
10.7.3. NUCLEAR MAGNETIC RESONANCE (NMR)
528(1)
10.7.4. QUANTUM DOTS
528(1)
10.8 SCOPE OF QUANTUM COMPUTING
528(1)
10.9 FROM CLASSICAL TO QUANTUM COMPUTING
529(1)
10.10 SUMMARY AND DISCUSSION
529(1)
10.11 EXERCISES
530(3)
10.11.1. QUESTIONS
530(1)
10.11.2. EXERCISES
531(1)
10.11.3. THOUGHT EXERCISES
532(1)
10.11.4. PROJECTS AND CHALLENGES
532(1)
10.12 REFERENCES
533(4)
11. AFTERWORDS
11.1 NEW PROSPECTS
537(1)
11.2 THE GROWTH OF NATURAL COMPUTING
538(1)
11.3 SOME LESSONS FROM NATURAL COMPUTING
539(1)
11.4 ARTIFICIAL INTELLIGENCE AND NATURAL COMPUTING
540(2)
11.4.1. THE BIRTH OF ARTIFICIAL INTELLIGENCE
540(1)
11.4.2. THE DIVORCE BETWEEN AI AND CI
540(1)
11.4.3. NATURAL COMPUTING AND THE OTHER NOMENCLATURES
541(1)
11.5 VISIONS
542(1)
11.6 REFERENCES
543(2)
APPENDIX A
A. GLOSSARY OF TERMS
545(26)
APPENDIX B
B. THEORETICAL BACKGROUND
571(40)
B.1 LINEAR ALGEBRA
571(14)
B.1.1. SETS AND SET OPERATIONS
571(1)
Sets
571(1)
Set Operations
572(1)
B.1.2. VECTORS AND VECTOR SPACES
572(3)
Scalar
572(1)
Vector
572(1)
Linear Vector Space
573(1)
Linear Vector Subspace
573(1)
Linear Variety
573(1)
Convex Set
573(1)
Linear Combinations, Spanning Sets, and Convex Combinations
574(1)
Linear Dependence and Independence
574(1)
Basis and Dimension of a Linear Vector Space
574(1)
Dot (Inner) Product
575(1)
Outer Product
575(1)
B.1.3. NORMS, PROJECTIONS, AND ORTHOGONALITY
575(3)
Norms, Semi-Norms and Quasi-Norms
575(2)
Orthogonal and Orthonormal Vectors
577(1)
Projecting a Vector along a Given Direction
577(1)
Orthonormal Vectors Generated from Linearly Independent Vectors
577(1)
B.1.4. MATRICES AND THEIR PROPERTIES
578(4)
Matrix
578(1)
Basic Operations Involving Vectors and Matrices
578(1)
Transpose and Square Matrices
579(1)
Trace
580(1)
Range and Rank
580(1)
Symmetry
580(1)
Inversion
580(1)
Pseudo-inversion
580(1)
Cofactor
581(1)
Determinant
581(1)
Adjoint
581(1)
Singularity
581(1)
Nullity
582(1)
Eigenvalues and Eigenvectors
582(1)
Positivity
582(1)
B.1.5. COMPLEX NUMBERS AND SPACES
582(3)
Complex Numbers
582(1)
Complex Conjugate and Absolute Value
583(1)
Complex Plane
583(1)
Polar Coordinates
583(1)
Exponential Form
584(1)
Complex Matrices
584(1)
Special Complex Matrices: Self-Adjoint (Hermitian), Unitary
584(1)
Hilbert Spaces
585(1)
Tensor Products
585(1)
B.2 STATISTICS
585(6)
B.2.1. ELEMENTARY CONCEPTS
585(1)
Population, Sample, Variables
585(1)
Branches of Statistics
586(1)
B.2.2. PROBABILITY
586(2)
Event and Sample Space
586(1)
Probability
586(1)
Conditional Probability
587(1)
Bayes Theorem
587(1)
Counting
588(1)
B.2.3. DISCRETE RANDOM VARIABLES
588(1)
Random Variable
588(1)
Discrete Random Variable
588(1)
Probability Distributions
589(1)
B.2.4. SUMMARY AND ASSOCIATION MEASURES
589(1)
Central Tendency and Dispersion Measures
589(1)
Association Measures
590(1)
B.2.5. ESTIMATION AND SAMPLE SIZES
590(1)
Point and Interval Estimators
590(1)
Confidence Interval
591(1)
B.3 THEORY OF COMPUTATION AND COMPLEXITY
591(5)
B.3.1. PRODUCTION SYSTEMS AND GRAMMARS
591(1)
B.3.2. UNIVERSAL TURING MACHINES
592(2)
B.3.3. COMPLEXITY THEORY
594(2)
B.4 OTHER CONCEPTS
596(12)
B.4.1. OPTIMIZATION
596(2)
B.4.2. LOGIC OF PROPOSITIONS
598(1)
B.4.3. THEORY OF NONLINEAR DYNAMICAL SYSTEMS
599(1)
B.4.4. GRAPH THEORY
600(3)
B.4.5. DATA CLUSTERING
603(1)
B.4.6. AFL INE TRANSFORMATIONS
604(3)
B.4.7. FOURIER TRANSFORMS
607(1)
B.5 BIBLIOGRAPHY
608(3)
APPENDIX C
C. A QUICK GUIDE TO THE LITERATURE
611(28)
C.1 INTRODUCTION
611(4)
C.1.1. COMMENTS ON SELECTED BIBLIOGRAPHY
611(1)
C.1.2. MAIN (GENERAL) JOURNALS
612(2)
C.1.3. MAIN CONFERENCES
614(1)
C.2 CONCEPTUALIZATION
615(2)
C.2.1. COMMENTS ON SELECTED BIBLIOGRAPHY
615(2)
C.3 EVOLUTIONARY COMPUTING
617(3)
C.3.1. COMMENTS ON SELECTED BIBLIOGRAPHY
617(2)
C.3.2. SPECIFIC JOURNALS
619(1)
C.3.3. SPECIFIC CONFERENCES
619(1)
C.4 NEUROCOMPUTING
620(3)
C.4.1. COMMENTS ON SELECTED BIBLIOGRAPHY
620(2)
C.4.2. SPECIFIC JOURNALS
622(1)
C.4.3. SPECIFIC CONFERENCES
623(1)
C.5 SWARM INTELLIGENCE
623(3)
C.5.1. COMMENTS ON SELECTED BIBLIOGRAPHY
623(2)
C.5.2. SPECIFIC JOURNALS
625(1)
C.5.3. SPECIFIC CONFERENCES
626(1)
C.6 IMMUNOCOMPUTING
626(3)
C.6.1. COMMENTS ON SELECTED BIBLIOGRAPHY
626(2)
C.6.2. SPECIFIC JOURNALS
628(1)
C.6.3. SPECIFIC CONFERENCES
628(1)
C.7 FRACTAL GEOMETRY OF NATURE
629(3)
C.7.1. COMMENTS ON SELECTED BIBLIOGRAPHY
629(1)
C.7.2. SPECIFIC JOURNALS
630(1)
C.7.3. SPECIFIC CONFERENCES
631(1)
C.8 ARTIFICIAL LIFE
632(1)
C.8.1. COMMENTS ON SELECTED BIBLIOGRAPHY
632(1)
C.8.2. SPECIFIC JOURNALS
633(1)
C.8.3. SPECIFIC CONFERENCES
633(1)
C.9 DNA COMPUTING
633(3)
C.9.1. COMMENTS ON SELECTED BIBLIOGRAPHY
633(1)
C.9.2. SPECIFIC JOURNALS
634(2)
C.9.3. SPECIFIC CONFERENCES
636(1)
C.10 QUANTUM COMPUTING
636(3)
C.10.1. COMMENTS ON SELECTED BIBLIOGRAPHY
636(1)
C.10.2. SPECIFIC JOURNALS
637(1)
C.10.3. SPECIFIC CONFERENCES
637(2)
INDEX 639

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