Bio-Inspired Networking /

Bio-inspired techniques are based on principles, or models, of biological systems. In general, natural systems present remarkable capabilities of resilience and adaptability. In this book, we explore how bio-inspired methods can solve different problems linked to computer networks.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: C�amara, Daniel (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Kidlington, Oxford : ISTE Press Ltd. ; Elsevier Ltd., 2015.
Temas:
Computer networks.
Computer Communication Networks
R�eseaux d'ordinateurs.
COMPUTERS > Computer Literacy.
COMPUTERS > Computer Science.
COMPUTERS > Data Processing.
COMPUTERS > Hardware > General.
COMPUTERS > Information Technology.
COMPUTERS > Machine Theory.
COMPUTERS > Reference.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: ch. 1 Evolution and Evolutionary Algorithms
  • 1.1. Brief introduction to evolution
  • 1.2. Mechanisms of evolution
  • 1.2.1. DNA code
  • 1.2.2. Mutation
  • 1.2.3. Sexual reproduction and recombination
  • 1.2.4. Natural selection
  • 1.2.5. Genetic drift
  • 1.3. Artificial evolution
  • 1.3.1. The basic process
  • 1.3.2. Limitations
  • 1.4. Applications on networks
  • 1.4.1.Network positioning
  • 1.4.2. Routing
  • 1.4.3. Other works
  • 1.5. Further reading
  • 1.6. Bibliography
  • ch. 2 Chemical Computing
  • 2.1. Artificial chemistry
  • 2.2. Applications on networks
  • 2.2.1. Data dissemination
  • 2.2.2. Routing
  • 2.3. Further reading
  • 2.4. Bibliography
  • ch. 3 Nervous System
  • 3.1. Nervous system hierarchy
  • 3.1.1. Central nervous system
  • 3.1.2. Peripheral nervous system
  • 3.2. The neuron
  • 3.3. The neocortex
  • 3.4. Speed and capacity
  • 3.5. Artificial neural networks
  • 3.5.1. The perceptron
  • 3.5.2. Interconnecting perceptrons
  • 3.5.3. Learning process.
  • Note continued: 3.5.4. The backpropagation algorithm
  • 3.6. Applications on networks
  • 3.6.1. ANN in intrusion detection systems
  • 3.6.2. Fault detection
  • 3.6.3. Routing
  • 3.7. Further reading
  • 3.8. Bibliography
  • ch. 4 Swarm Intelligence (SI)
  • 4.1. Ant colony optimization
  • 4.2. Applications on networks
  • 4.2.1. Ants colony on routing
  • 4.2.2. Ants colony on intrusion detection
  • 4.3. Particle swarm optimization
  • 4.4. Applications on networks
  • 4.4.1. Particle swarm on node positioning
  • 4.4.2. Particle swarm on intrusion detection
  • 4.5. Further reading
  • 4.6. Bibliography.

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