Knowles, Joshua

Multiobjective Problem Solving from Nature

Multiobjective Problem Solving from Nature


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ISBN: 9783540729648
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Table of contents

1. Introduction: Problem Solving, EC and EMO
Joshua Knowles, David Corne, Kalyanmoy Deb

Part I. Exploiting Multiple Objectives: From Problems to Solutions

2. Multiobjective Optimization and Coevolution
Sevan Gregory Ficici

3. Constrained Optimization via Multiobjective Evolutionary Algorithms
Efrén Mezura-Montes, Carlos A. Coello Coello

4. Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms
Lam T. Bui, Minh-Ha Nguyen, Jürgen Branke, Hussein A. Abbass

5. Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms
Vincenzo Cutello, Giuseppe Narzisi, Giuseppe Nicosia

6. Can Single-Objective Optimization Profit from Multiobjective Optimization?
Frank Neumann, Ingo Wegener

7. Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making
Julia Handl, Joshua Knowles

Part II. Machine Learning with Multiple Objectives

8. Multiobjective Supervised Learning
Jonathan E. Fieldsend, Richard M. Everson

9. Reducing Bloat in GP with Multiple Objectives
Stefan Bleuler, Johannes Bader, Eckart Zitzler

10. Multiobjective GP for Human-Understandable Models: A Practical Application
Katya Rodríguez-Vázquez, Peter J. Fleming

11. Multiobjective Classification Rule Mining
Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima

Part III. Multiple Objectives in Design and Engineering

12. Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization
Kalyanmoy Deb, Aravind Srinivasan

13. User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria
Ian C. Parmee, Johnson A. R. Abraham, Azahar Machwe

14. Multi-competence Cybernetics: The Study of Multiobjective Artificial Systems and Multi-fitness Natural Systems
Amiram Moshaiov

Part IV. Scaling up Multiobjective Optimization

15. Fitness Assignment Methods for Many-Objective Problems
Evan J. Hughes

16. Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms
Yaochu Jin, Aimin Zhou, Qingfu Zhang, Bernhard Sendhoff, Edward Tsang

17. Objective Set Compression
Edwin D. de Jong, Anthony Bucci

18. On Handling a Large Number of Objectives A Posteriori and During Optimization
Dimo Brockhoff, Dhish Kumar Saxena, Kalyanmoy Deb, Eckart Zitzler


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Keywords: COMPUTERS / Computer Science COM014000

Publication year
Information Technology, Telecommunications

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