Multiobjective Problem Solving from Nature (eBook)

From Concepts to Applications

Joshua Knowles, David Corne, Kalyanmoy Deb (Herausgeber)

eBook Download: PDF

2007 | 2008
XVI, 411 Seiten
Springer Berlin (Verlag)
978-3-540-72964-8 (ISBN)

Infos

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.

Inhalt

Preface 6
Contents 8
List of Contributors 11
Introduction: Problem Solving, EC and EMO 15
Exploiting Multiple Objectives: From Problems to Solutions 43
Multiobjective Optimization and Coevolution 44
Constrained Optimization via Multiobjective Evolutionary Algorithms 66
Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms 89
Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms 104
Can Single-Objective Optimization Profit from Multiobjective Optimization? 126
Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making 142
Machine Learning with Multiple Objectives 163
Multiobjective Supervised Learning 164
Reducing Bloat in GP with Multiple Objectives 186
Multiobjective GP for Human-Understandable Models: A Practical Application 210
Multiobjective Classification Rule Mining 228
Multiple Objectives in Design and Engineering 250
Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization 251
Principles Through Multiobjective Evolutionary Optimization 251
User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria 271
Multi-competence Cybernetics: The Study of Multiobjective Artificial Systems and Multi- fitness Natural Systems 292
Scaling up Multiobjective Optimization 312
Fitness Assignment Methods for Many- Objective Problems 313
Modeling Regularity to Improve Scalability of Model- Based Multiobjective Optimization Algorithms 336
Objective Set Compression 361
On Handling a Large Number of Objectives A Posteriori and During Optimization 381
Index 408

Produkt-Details

• Erscheint lt. Verlag: 15.11.2007
• Reihe/Serie: Natural Computing Series
• Reihe/Serie: Natural Computing Series
• Zusatzinfo: XVI, 411 p.
• Verlagsort: Berlin
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Mathematik / Informatik ► Mathematik
• Themenwelt: Technik
• Schlagworte: algorithms • classification • Computational Intelligence • Decision Making • Design • Engineering design • Evolution • evolutionary algorithm • evolutionary computation • learning • machine learning • Metaheuristics • Modeling • Multicriterion decision analysis • Multicriterion optimization • Multiobjective Optimization • Multi-Objective Optimization • Natural Computing • Optimization • Problem Solving • Problem-Solving
• ISBN-10: 3-540-72964-X / 354072964X
• ISBN-13: 978-3-540-72964-8 / 9783540729648