Self-Adaptive Heuristics for Evolutionary Computation

Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adapt...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Kramer, Oliver (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Studies in Computational Intelligence, 147
Temas:
Computer-aided engineering.
Engineering mathematics.
Engineering-Data processing.
Artificial intelligence.
Computer-Aided Engineering (CAD, CAE) and Design.
Mathematical and Computational Engineering Applications.
Artificial Intelligence.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • I: Foundations of Evolutionary Computation
  • Evolutionary Algorithms
  • Self-Adaptation
  • II: Self-Adaptive Operators
  • Biased Mutation for Evolution Strategies
  • Self-Adaptive Inversion Mutation
  • Self-Adaptive Crossover
  • III: Constraint Handling
  • Constraint Handling Heuristics for Evolution Strategies
  • IV: Summary
  • Summary and Conclusion
  • V: Appendix
  • Continuous Benchmark Functions
  • Discrete Benchmark Functions.

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