Nature-Inspired Algorithms for Optimisation

Nature-Inspired Algorithms have been gaining much popularity in recent years due to the fact that many real-world optimisation problems have become increasingly large, complex and dynamic. The size and complexity of the problems nowadays require the development of methods and solutions whose efficie...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Chiong, Raymond (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Studies in Computational Intelligence, 193
Temas:
Engineering mathematics.
Engineering-Data processing.
Artificial intelligence.
Operations research.
Mathematical and Computational Engineering Applications.
Artificial Intelligence.
Operations Research and Decision Theory.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Section I: Introduction
  • Why Is Optimization Difficult?
  • The Rationale Behind Seeking Inspiration from Nature
  • Section II: Evolutionary Intelligence
  • The Evolutionary-Gradient-Search Procedure in Theory and Practice
  • The Evolutionary Transition Algorithm: Evolving Complex Solutions Out of Simpler Ones
  • A Model-Assisted Memetic Algorithm for Expensive Optimization Problems
  • A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
  • Differential Evolution with Fitness Diversity Self-adaptation
  • Central Pattern Generators: Optimisation and Application
  • Section III: Collective Intelligence
  • Fish School Search
  • Magnifier Particle Swarm Optimization
  • Improved Particle Swarm Optimization in Constrained Numerical Search Spaces
  • Applying River Formation Dynamics to Solve NP-Complete Problems
  • Section IV: Social-Natural Intelligence
  • Algorithms Inspired in Social Phenomena
  • Artificial Immune Systems for Optimization
  • Section V: Multi-Objective Optimisation
  • Ranking Methods in Many-Objective Evolutionary Algorithms
  • On the Effect of Applying a Steady-State Selection Scheme in the Multi-Objective Genetic Algorithm NSGA-II
  • Improving the Performance of Multiobjective Evolutionary Optimization Algorithms Using Coevolutionary Learning
  • Evolutionary Optimization for Multiobjective Portfolio Selection under Markowitz's Model with Application to the Caracas Stock Exchange.

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