Multiobjective Problem Solving from Nature

From Concepts to Applications

Inbunden, Engelska, 2007

Av Joshua Knowles, David Corne, Kalyanmoy Deb

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This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concept of multiobjective optimization can be used to reformulate and resolve problems in areas such as constrained optimization, co-evolution, classification, inverse modeling, and design.

Produktinformation

Utgivningsdatum2007-12-05
Mått155 x 235 x 29 mm
Vikt805 g
FormatInbunden
SpråkEngelska
SerieNatural Computing Series
Antal sidor411
Upplaga2008
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540729631

Tillhör följande kategorier

Matematik inom Naturvetenskap och teknik
Optimering inom Naturvetenskap och teknik
Teknik: allmänt inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it

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.