Multiobjective Problem Solving from Nature From Concepts to Applications /

Multiobjective problems involve several competing measures of solution quality, and multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community over the past 10 years. This is an advanced text ai...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Knowles, Joshua (Editor ), Corne, David (Editor ), Deb, Kalyanmoy (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:1st ed. 2008.
Colección:Natural Computing Series,
Temas:
Artificial intelligence.
Mathematical optimization.
Engineering design.
Computer science.
Artificial Intelligence.
Optimization.
Engineering Design.
Theory of Computation.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Introduction: Problem Solving, EC and EMO
  • Introduction: Problem Solving, EC and EMO
  • Exploiting Multiple Objectives: From Problems to Solutions
  • Multiobjective Optimization and Coevolution
  • Constrained Optimization via Multiobjective Evolutionary Algorithms
  • Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms
  • Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms
  • Can Single-Objective Optimization Profit from Multiobjective Optimization?
  • Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making
  • Machine Learning with Multiple Objectives
  • Multiobjective Supervised Learning
  • Reducing Bloat in GP with Multiple Objectives
  • Multiobjective GP for Human-Understandable Models: A Practical Application
  • Multiobjective Classification Rule Mining
  • Multiple Objectives in Design and Engineering
  • Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization
  • User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria
  • Multi-competence Cybernetics: The Study of Multiobjective Artificial Systems and Multi-fitness Natural Systems
  • Scaling up Multiobjective Optimization
  • Fitness Assignment Methods for Many-Objective Problems
  • Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms
  • Objective Set Compression
  • On Handling a Large Number of Objectives A Posteriori and During Optimization.

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