Fundamentals of natural computing: basic concepts, algorithms, and applications
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton u.a.
Chapman & Hall/CRC
2006
|
| Schriftenreihe: | Chapman & Hall/CRC computer and information science series
|
| Schlagworte: | |
| Online-Zugang: | Publisher description Inhaltsverzeichnis |
| Beschreibung: | 662 S. Ill., graph. Darst. |
| ISBN: | 1584886439 |
Internformat
MARC
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| 100 | 1 | |a Castro, Leandro N. de |d 1974- |e Verfasser |0 (DE-588)124173667 |4 aut | |
| 245 | 1 | 0 | |a Fundamentals of natural computing |b basic concepts, algorithms, and applications |c Leandro Nunes de Castro |
| 264 | 1 | |a Boca Raton u.a. |b Chapman & Hall/CRC |c 2006 | |
| 300 | |a 662 S. |b Ill., graph. Darst. | ||
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| 490 | 0 | |a Chapman & Hall/CRC computer and information science series | |
| 650 | 4 | |a Ordinateurs moléculaires | |
| 650 | 4 | |a Ordinateurs quantiques | |
| 650 | 4 | |a Programmation évolutive | |
| 650 | 4 | |a Réseaux neuronaux (Informatique) | |
| 650 | 4 | |a Evolutionary programming (Computer science) | |
| 650 | 4 | |a Neural networks (Computer science) | |
| 650 | 4 | |a Quantum computers | |
| 650 | 4 | |a Molecular computers | |
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Datensatz im Suchindex
| _version_ | 1804135455265914880 |
|---|---|
| adam_text | Contents
1.
1.1
1.1.1.
1.2
1.3
1.4
1.4.1.
1.4.2.
1.4.3.
1.5
1.6
1.7
1.8
2.
2.1
2.1.1.
2.1.2.
2.2
2.2.1.
2.2.2.
2.2.3.
Connectivity
Stigmergy
2.2.4.
Learning
Evolution
2.2.5.
Positive Feedback
Negative Feedback
2.2.6.
Characteristics of Self-Organization
Alternatives to Self-Organization
2.2.7.
Complexity
Emergence
Reductionism
2.2.8.
Bottom-Up
Top-Down
2.2.9.
23
2.4
2.4.1.
2.4.2.
2.4.3.
2.5
PART I- Computing Inspimed by Nature
3.
3.1
3.2
3.2.1.
3.3
3.3.1.
3.3.2.
Basic Principles of Statistical Thermodynamics
The Simulated Annealing Algorithm
From Statistical Thermodynamics to Computing
3.3.3.
3.4
3.4.1.
3.4.2.
3.4.3.
3.4.4.
3.4.5.
3.4.6.
3.5
3.5.1.
3.5.2.
Roulette Wheel Selection
Crossover
Mutation
3.5.3.
A Step by Step Example: Pattern Recognition (Learning)
Numerical Function Optimization
3.5.4.
3.6
3.6.1.
Selection
Crossover
Mutation
3.6.2.
Selection
Mutation
3.6.3.
Crossover
Mutation
3.6.4.
ES: Engineering Design....................................................................................108
EP: Parameter
GP:
3.7
3.8
3.9
3.9.1.
3.10
3.10.1.
3.10.2.
3.10.3.
3.10.4.
3.11
4.
4.1
4.2
4.2.1.
Neurons and Synapses
Networks, Layers, and Maps
4.2.2.
4.3
4.3.1.
The McCulloch and Pitts Neuron
A Basic Integrate-and-Fire Neuron
The Generic Neurocomputing Neuron
4.3.2
Single-Layer Feedforward Networks
Multi-Layer Feedforward Networks
Recurrent Networks
4.3.3
Supervised Learning
Unsupervised Learning
Reinforcement Learning
4.4
4.4.1
Biological Basis of Hebbian Synaptic Modification
4.4.2
Linear Separability
Simple Perceptron for Pattern Classification
Multiple Output Perceptron for Pattern Classification
Examples of Application
4.4.3
LMS Algorithm (Delta Rule)
Error Surfaces
4.4.4
The Backpropagation Learning Algorithm
Universal Function Approximation
Some Practical Aspects
Biological Plausibility of Backpropagation
Examples of Application
4.4.5
Self-Organizing Map Learning Algorithm
Biological Basis and Inspiration for the Self-Organizing Map
Examples of Applications
4.4.6
Recurrent Neural Networks as Nonlinear Dynamical Systems
Discrete Hopfield Network
Spurious Attractors
Example of Application
4.5
4.6
4.7
4.8
4.8.1
4.8.2
4.8.3
4.8.4
4.9
5.
5.1.
5.2.
5.2.1
5.2.2
Stigmergy
5.2.3
The Simple Ant Colony Optimization Algorithm (S-ACO)
General-Purpose Ant Colony Optimization Algorithm
Selected Applications from the Literature: A Brief Description
Scope of ACO Algorithms
From Natural to Artificial Ants
5.2.4
Stigmergy
5.2.5
The Standard Ant Clustering Algorithm
Selected Applications from the Literature: A Brief Description
Scope of Ant Clustering Algorithms
From Natural to Artificial Ants
5.2.6
5.3.
5.3.1
5.3.2
5.3.3
Cooperative Box Pushing
Recruitment of Nestmates
5.3.4
5.3.5
5.4.
5.4.1
5.4.2
Optimization of Neural Network Weights
Numerical Function Optimization
5.4.3
5.4.4
5.4.5
5.5.
5.6.
5.6.1
5.6.2
5.6.3
5.6.4
5.7.
6.
6.1
6.2
6.2.1.
6.2.2.
6.2.3.
Adaptation via Clonal Selection
Clonal Selection and Darwinian Evolution
6.2.4.
6.2.5.
Adaptation and Learning via Immune Network
6.2.6.
6.2.7.
6.3
6.3.1.
6.3.2.
6.3.3.
6.4
6.4.1.
Evolution of the Genetic Encoding of Antibodies
Antigenic Coverage and Evolution of Antibody Gene Libraries
Generating Antibodies for Job Shop Scheduling
6.5
6.5.1.
6.5.2.
6.5.3.
Network Intrusion Detection
Breast Cancer Diagnosis
6.6
6.6.1.
6.6.2.
6.6.3.
Pattern Recognition
Multimodal
6.7
6.7.1.
6.7.2.
6.7.3.
A Recommender System
Data Compression and Clustering
6.8
6.9
6.10
6.11
6.11.1.
6.11.2.
6.11.3.
6.11.4.
6.12
PART
Phenomena in Computers
7.
7.1
7.2
7.2.1.
7.2.2.
7.2.3.
7.2.4.
7.3
7.3.1.
7.3.2.
7.3.3.
7.3.4.
Fractal Patterns
7.3.5.
7.4
7.4.1.
7.4.2.
7.4.3.
7.4.4.
7.5
7.5.1.
Deterministic Iterated Function System (DIFS)
Random Iterated Fraction System (MFS)
7.5.2.
7.5.3.
7.5.4.
7.6
7.6.1.
7.6.2.
7.6.3.
7.7
7.7.1.
7.7.2.
Particle Generation
Particle Attributes
Particle Extinction
Particle Dynamics
Particle Rendering
7.7.3.
7.7.4.
7.8
7.8.1.
7.8.2.
7.9
7.10
7.11
7.11.1.
7.11.2.
7.11.3.
7.11.4.
7.12
8.
8.1
8.1.1.
8.2
8.2.1.
8.2.2.
8.2.3.
8.2.4.
8.2.5. ALife
8.3
8.3.1.
Discussion and Applications
8.3.2.
10.4.3.
Generalizations of the Hadamard Gate
10.4.4.
10.4.5.
10.4.6.
Dense Coding
Quantum Teleportation
10.5
10.5.1.
10.5.2.
10.5.3.
10.6
10.6.1.
10.6.2.
10.6.3.
Quantum Fourier Transform
Factorization
10.6.4.
10.7
description............................................................................................................
10.7.1.
10.7.2.
10.7.3.
10.7.4.
10.8
10.9
10.10
10.11
10.11.1.
10.11.2.
10.11.3.
10.11.4.
10.12
11.
11.1
11.2
11.3
11.4
11.4.1.
11.4.2.
11.4.3.
11.5
11.6
APPENDIX A
A. GLOSSARY OF TERMS........
APPENDIX
B.
B.1 LINEAR ALGEBRA
B.I.I. Sets and Set Operations
Sets
Set Operations
B.1.2. Vectors and Vector Spaces
Scalar
Vector
Linear Vector Space
Linear Vector Subspace
Linear Variety
Convex Set
Linear Combinations, Spanning Sets, and Convex Combinations
Linear Dependence and Independence
Basis and Dimension of a Linear Vector Space
Dot (Inner) Product
Outer Product
B.1.3. Norms, Projections, and Orthogonality
Norms, Semi-Norms and Quasi-Norms
Orthogonal and
Projecting a Vector along a Given Direction
Orthonormal
B.I.
Matrix
Basic Operations Involving Vectors and Matrices
Transpose and Square Matrices
Trace
Range and Rank
Symmetry
Inversion
Pseudo-inversion
Cofactor
Determinant
Adjoint
Singularity
Nullity
Eigenvalues and Eigenvectors
Positivity
B.1.5. Complex Numbers and Spaces
Complex Numbers
Complex Conjugate and Absolute Value
Complex Plane
Polar Coordinates
Exponential Form
Complex Matrices
Special Complex Matrices: Self-Adjoint (Hermitian), Unitary
Hubert Spaces...................................................................................................585
Tensor
B.2
B.2.1. Elementary Concepts
Population, Sample, Variables
Branches of Statistics
B.2.2. Probability
Event and Sample Space
Probability
Conditional Probability
Bayes
Counting
B.2.3. Discrete Random Variables
Random Variable
Discrete Random Variable
Probability Distributions
B.2.4. Summary and Association Measures
Central Tendency and Dispersion Measures
Association Measures
B.2.5. Estimation and Sample Sizes
Point and Interval Estimators
Confidence Interval
B.3 THEORY OF COMPUTATION AND COMPLEXITY
B.3.1. Production Systems and Grammars
B.3.2. Universal Turing Machines
B.3.3. Complexity Theory
B.4 OTHER CONCEPTS
B.4.1. Optimization
B.4.2. Logic of Propositions
B.4.3. Theory of Nonlinear Dynamical Systems
B.4.4. Graph Theory
B.4.5. Data Clustering
B.4.6.
B.4.7. Fourier Transforms
B.5 BIBLIOGRAPHY
APPENDIX
С.
C.1 INTRODUCTION
СІЛ.
СЛ.2.
СЛ
C.2 CONCEPTUALIZATION
С.2Л.
C.3 EVOLUTIONARY COMPUTING
С.ЗЛ.
C.3.2. Specific Journals
C.3.3.
C.4
C.4.1. Comments on Selected Bibliography
C.4.2. Specific Journals
C.4.3. Specific Conferences
C.5 SWARM INTELLIGENCE
C.5.1. Comments on Selected Bibliography
C.5.2. Specific Journals
C.5.3. Specific Conferences
C.6
C.6.1. Comments on Selected Bibliography
C.6.2. Specific Journals
C.6.3. Specific Conferences
C.7 FRACTAL GEOMETRY OF NATURE..
C.7.1. Comments on Selected Bibliography
C.7.2. Specific Journals
C.7.3. Specific Conferences
C.8 ARTIFICIAL LIFE...............................................................................
C.8.1. Comments on Selected Bibliography
C.8.2. Specific Journals
C.8.3. Specific Conferences
C.9
C.9.1. Comments on Selected Bibliography
C.9.2. Specific Journals
C.9.3. Specific Conferences
CIO QUANTUM COMPUTING
С
С
C.10.3. Specific Conferences
INDEX
|
| adam_txt |
Contents
1.
1.1
1.1.1.
1.2
1.3
1.4
1.4.1.
1.4.2.
1.4.3.
1.5
1.6
1.7
1.8
2.
2.1
2.1.1.
2.1.2.
2.2
2.2.1.
2.2.2.
2.2.3.
Connectivity
Stigmergy
2.2.4.
Learning
Evolution
2.2.5.
Positive Feedback
Negative Feedback
2.2.6.
Characteristics of Self-Organization
Alternatives to Self-Organization
2.2.7.
Complexity
Emergence
Reductionism
2.2.8.
Bottom-Up
Top-Down
2.2.9.
23
2.4
2.4.1.
2.4.2.
2.4.3.
2.5
PART I- Computing Inspimed by Nature
3.
3.1
3.2
3.2.1.
3.3
3.3.1.
3.3.2.
Basic Principles of Statistical Thermodynamics
The Simulated Annealing Algorithm
From Statistical Thermodynamics to Computing
3.3.3.
3.4
3.4.1.
3.4.2.
3.4.3.
3.4.4.
3.4.5.
3.4.6.
3.5
3.5.1.
3.5.2.
Roulette Wheel Selection
Crossover
Mutation
3.5.3.
A Step by Step Example: Pattern Recognition (Learning)
Numerical Function Optimization
3.5.4.
3.6
3.6.1.
Selection
Crossover
Mutation
3.6.2.
Selection
Mutation
3.6.3.
Crossover
Mutation
3.6.4.
ES: Engineering Design.108
EP: Parameter
GP:
3.7
3.8
3.9
3.9.1.
3.10
3.10.1.
3.10.2.
3.10.3.
3.10.4.
3.11
4.
4.1
4.2
4.2.1.
Neurons and Synapses
Networks, Layers, and Maps
4.2.2.
4.3
4.3.1.
The McCulloch and Pitts Neuron
A Basic Integrate-and-Fire Neuron
The Generic Neurocomputing Neuron
4.3.2
Single-Layer Feedforward Networks
Multi-Layer Feedforward Networks
Recurrent Networks
4.3.3
Supervised Learning
Unsupervised Learning
Reinforcement Learning
4.4
4.4.1
Biological Basis of Hebbian Synaptic Modification
4.4.2
Linear Separability
Simple Perceptron for Pattern Classification
Multiple Output Perceptron for Pattern Classification
Examples of Application
4.4.3
LMS Algorithm (Delta Rule)
Error Surfaces
4.4.4
The Backpropagation Learning Algorithm
Universal Function Approximation
Some Practical Aspects
Biological Plausibility of Backpropagation
Examples of Application
4.4.5
Self-Organizing Map Learning Algorithm
Biological Basis and Inspiration for the Self-Organizing Map
Examples of Applications
4.4.6
Recurrent Neural Networks as Nonlinear Dynamical Systems
Discrete Hopfield Network
Spurious Attractors
Example of Application
4.5
4.6
4.7
4.8
4.8.1
4.8.2
4.8.3
4.8.4
4.9
5.
5.1.
5.2.
5.2.1
5.2.2
Stigmergy
5.2.3
The Simple Ant Colony Optimization Algorithm (S-ACO)
General-Purpose Ant Colony Optimization Algorithm
Selected Applications from the Literature: A Brief Description
Scope of ACO Algorithms
From Natural to Artificial Ants
5.2.4
Stigmergy
5.2.5
The Standard Ant Clustering Algorithm
Selected Applications from the Literature: A Brief Description
Scope of Ant Clustering Algorithms
From Natural to Artificial Ants
5.2.6
5.3.
5.3.1
5.3.2
5.3.3
Cooperative Box Pushing
Recruitment of Nestmates
5.3.4
5.3.5
5.4.
5.4.1
5.4.2
Optimization of Neural Network Weights
Numerical Function Optimization
5.4.3
5.4.4
5.4.5
5.5.
5.6.
5.6.1
5.6.2
5.6.3
5.6.4
5.7.
6.
6.1
6.2
6.2.1.
6.2.2.
6.2.3.
Adaptation via Clonal Selection
Clonal Selection and Darwinian Evolution
6.2.4.
6.2.5.
Adaptation and Learning via Immune Network
6.2.6.
6.2.7.
6.3
6.3.1.
6.3.2.
6.3.3.
6.4
6.4.1.
Evolution of the Genetic Encoding of Antibodies
Antigenic Coverage and Evolution of Antibody Gene Libraries
Generating Antibodies for Job Shop Scheduling
6.5
6.5.1.
6.5.2.
6.5.3.
Network Intrusion Detection
Breast Cancer Diagnosis
6.6
6.6.1.
6.6.2.
6.6.3.
Pattern Recognition
Multimodal
6.7
6.7.1.
6.7.2.
6.7.3.
A Recommender System
Data Compression and Clustering
6.8
6.9
6.10
6.11
6.11.1.
6.11.2.
6.11.3.
6.11.4.
6.12
PART
Phenomena in Computers
7.
7.1
7.2
7.2.1.
7.2.2.
7.2.3.
7.2.4.
7.3
7.3.1.
7.3.2.
7.3.3.
7.3.4.
Fractal Patterns
7.3.5.
7.4
7.4.1.
7.4.2.
7.4.3.
7.4.4.
7.5
7.5.1.
Deterministic Iterated Function System (DIFS)
Random Iterated Fraction System (MFS)
7.5.2.
7.5.3.
7.5.4.
7.6
7.6.1.
7.6.2.
7.6.3.
7.7
7.7.1.
7.7.2.
Particle Generation
Particle Attributes
Particle Extinction
Particle Dynamics
Particle Rendering
7.7.3.
7.7.4.
7.8
7.8.1.
7.8.2.
7.9
7.10
7.11
7.11.1.
7.11.2.
7.11.3.
7.11.4.
7.12
8.
8.1
8.1.1.
8.2
8.2.1.
8.2.2.
8.2.3.
8.2.4.
8.2.5. ALife
8.3
8.3.1.
Discussion and Applications
8.3.2.
10.4.3.
Generalizations of the Hadamard Gate
10.4.4.
10.4.5.
10.4.6.
Dense Coding
Quantum Teleportation
10.5
10.5.1.
10.5.2.
10.5.3.
10.6
10.6.1.
10.6.2.
10.6.3.
Quantum Fourier Transform
Factorization
10.6.4.
10.7
description.
10.7.1.
10.7.2.
10.7.3.
10.7.4.
10.8
10.9
10.10
10.11
10.11.1.
10.11.2.
10.11.3.
10.11.4.
10.12
11.
11.1
11.2
11.3
11.4
11.4.1.
11.4.2.
11.4.3.
11.5
11.6
APPENDIX A
A. GLOSSARY OF TERMS.
APPENDIX
B.
B.1 LINEAR ALGEBRA
B.I.I. Sets and Set Operations
Sets
Set Operations
B.1.2. Vectors and Vector Spaces
Scalar
Vector
Linear Vector Space
Linear Vector Subspace
Linear Variety
Convex Set
Linear Combinations, Spanning Sets, and Convex Combinations
Linear Dependence and Independence
Basis and Dimension of a Linear Vector Space
Dot (Inner) Product
Outer Product
B.1.3. Norms, Projections, and Orthogonality
Norms, Semi-Norms and Quasi-Norms
Orthogonal and
Projecting a Vector along a Given Direction
Orthonormal
B.I.
Matrix
Basic Operations Involving Vectors and Matrices
Transpose and Square Matrices
Trace
Range and Rank
Symmetry
Inversion
Pseudo-inversion
Cofactor
Determinant
Adjoint
Singularity
Nullity
Eigenvalues and Eigenvectors
Positivity
B.1.5. Complex Numbers and Spaces
Complex Numbers
Complex Conjugate and Absolute Value
Complex Plane
Polar Coordinates
Exponential Form
Complex Matrices
Special Complex Matrices: Self-Adjoint (Hermitian), Unitary
Hubert Spaces.585
Tensor
B.2
B.2.1. Elementary Concepts
Population, Sample, Variables
Branches of Statistics
B.2.2. Probability
Event and Sample Space
Probability
Conditional Probability
Bayes
Counting
B.2.3. Discrete Random Variables
Random Variable
Discrete Random Variable
Probability Distributions
B.2.4. Summary and Association Measures
Central Tendency and Dispersion Measures
Association Measures
B.2.5. Estimation and Sample Sizes
Point and Interval Estimators
Confidence Interval
B.3 THEORY OF COMPUTATION AND COMPLEXITY
B.3.1. Production Systems and Grammars
B.3.2. Universal Turing Machines
B.3.3. Complexity Theory
B.4 OTHER CONCEPTS
B.4.1. Optimization
B.4.2. Logic of Propositions
B.4.3. Theory of Nonlinear Dynamical Systems
B.4.4. Graph Theory
B.4.5. Data Clustering
B.4.6.
B.4.7. Fourier Transforms
B.5 BIBLIOGRAPHY
APPENDIX
С.
C.1 INTRODUCTION
СІЛ.
СЛ.2.
СЛ
C.2 CONCEPTUALIZATION
С.2Л.
C.3 EVOLUTIONARY COMPUTING
С.ЗЛ.
C.3.2. Specific Journals
C.3.3.
C.4
C.4.1. Comments on Selected Bibliography
C.4.2. Specific Journals
C.4.3. Specific Conferences
C.5 SWARM INTELLIGENCE
C.5.1. Comments on Selected Bibliography
C.5.2. Specific Journals
C.5.3. Specific Conferences
C.6
C.6.1. Comments on Selected Bibliography
C.6.2. Specific Journals
C.6.3. Specific Conferences
C.7 FRACTAL GEOMETRY OF NATURE.
C.7.1. Comments on Selected Bibliography
C.7.2. Specific Journals
C.7.3. Specific Conferences
C.8 ARTIFICIAL LIFE.
C.8.1. Comments on Selected Bibliography
C.8.2. Specific Journals
C.8.3. Specific Conferences
C.9
C.9.1. Comments on Selected Bibliography
C.9.2. Specific Journals
C.9.3. Specific Conferences
CIO QUANTUM COMPUTING
С
С
C.10.3. Specific Conferences
INDEX |
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| any_adam_object_boolean | 1 |
| author | Castro, Leandro N. de 1974- |
| author_GND | (DE-588)124173667 |
| author_facet | Castro, Leandro N. de 1974- |
| author_role | aut |
| author_sort | Castro, Leandro N. de 1974- |
| author_variant | l n d c lnd lndc |
| building | Verbundindex |
| bvnumber | BV021649546 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.618 |
| callnumber-search | QA76.618 |
| callnumber-sort | QA 276.618 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 152 |
| ctrlnum | (OCoLC)64230369 (DE-599)BVBBV021649546 |
| dewey-full | 005.1 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.1 |
| dewey-search | 005.1 |
| dewey-sort | 15.1 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| id | DE-604.BV021649546 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:02:37Z |
| indexdate | 2024-07-09T20:40:46Z |
| institution | BVB |
| isbn | 1584886439 |
| language | English |
| lccn | 2006006350 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014864215 |
| oclc_num | 64230369 |
| open_access_boolean | |
| owner | DE-384 DE-739 DE-20 DE-859 DE-1028 DE-573 |
| owner_facet | DE-384 DE-739 DE-20 DE-859 DE-1028 DE-573 |
| physical | 662 S. Ill., graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Chapman & Hall/CRC |
| record_format | marc |
| series2 | Chapman & Hall/CRC computer and information science series |
| spelling | Castro, Leandro N. de 1974- Verfasser (DE-588)124173667 aut Fundamentals of natural computing basic concepts, algorithms, and applications Leandro Nunes de Castro Boca Raton u.a. Chapman & Hall/CRC 2006 662 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC computer and information science series Ordinateurs moléculaires Ordinateurs quantiques Programmation évolutive Réseaux neuronaux (Informatique) Evolutionary programming (Computer science) Neural networks (Computer science) Quantum computers Molecular computers Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Molekulare Bioinformatik (DE-588)4531334-9 gnd rswk-swf Evolutionäre Programmierung (DE-588)4366915-3 gnd rswk-swf Quantencomputer (DE-588)4533372-5 gnd rswk-swf Evolutionäre Programmierung (DE-588)4366915-3 s Neuronales Netz (DE-588)4226127-2 s Quantencomputer (DE-588)4533372-5 s Molekulare Bioinformatik (DE-588)4531334-9 s DE-604 http://www.loc.gov/catdir/enhancements/fy0654/2006006350-d.html Publisher description Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014864215&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Castro, Leandro N. de 1974- Fundamentals of natural computing basic concepts, algorithms, and applications Ordinateurs moléculaires Ordinateurs quantiques Programmation évolutive Réseaux neuronaux (Informatique) Evolutionary programming (Computer science) Neural networks (Computer science) Quantum computers Molecular computers Neuronales Netz (DE-588)4226127-2 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd Evolutionäre Programmierung (DE-588)4366915-3 gnd Quantencomputer (DE-588)4533372-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4531334-9 (DE-588)4366915-3 (DE-588)4533372-5 |
| title | Fundamentals of natural computing basic concepts, algorithms, and applications |
| title_auth | Fundamentals of natural computing basic concepts, algorithms, and applications |
| title_exact_search | Fundamentals of natural computing basic concepts, algorithms, and applications |
| title_exact_search_txtP | Fundamentals of natural computing basic concepts, algorithms, and applications |
| title_full | Fundamentals of natural computing basic concepts, algorithms, and applications Leandro Nunes de Castro |
| title_fullStr | Fundamentals of natural computing basic concepts, algorithms, and applications Leandro Nunes de Castro |
| title_full_unstemmed | Fundamentals of natural computing basic concepts, algorithms, and applications Leandro Nunes de Castro |
| title_short | Fundamentals of natural computing |
| title_sort | fundamentals of natural computing basic concepts algorithms and applications |
| title_sub | basic concepts, algorithms, and applications |
| topic | Ordinateurs moléculaires Ordinateurs quantiques Programmation évolutive Réseaux neuronaux (Informatique) Evolutionary programming (Computer science) Neural networks (Computer science) Quantum computers Molecular computers Neuronales Netz (DE-588)4226127-2 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd Evolutionäre Programmierung (DE-588)4366915-3 gnd Quantencomputer (DE-588)4533372-5 gnd |
| topic_facet | Ordinateurs moléculaires Ordinateurs quantiques Programmation évolutive Réseaux neuronaux (Informatique) Evolutionary programming (Computer science) Neural networks (Computer science) Quantum computers Molecular computers Neuronales Netz Molekulare Bioinformatik Evolutionäre Programmierung Quantencomputer |
| url | http://www.loc.gov/catdir/enhancements/fy0654/2006006350-d.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014864215&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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