Multiobjective Problem Solving from Nature: From Concepts to Applications

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Joshua Knowles, David Corne, Kalyanmoy Deb
Springer Science & Business Media, Nov 15, 2007 - Computers - 411 pages
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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 aimed at researchers and practitioners in the area of search and optimization.

The book focuses on how MOEAs and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concepts of multiobjective optimization can be used to reformulate and resolve problems in broad areas such as constrained optimization, coevolution, classification, inverse modelling and design. The book is distinguished from other texts on MOEAs in that it is not primarily about the algorithms, nor specific applications, but about the concepts and processes involved in solving problems using a multiobjective approach. Each chapter contributes to the central, deep concepts and themes of the book: evaluating the utility of the multiobjective approach; discussing alternative problem formulations; showing how problem formulation affects the search process; and examining solution selection and decision making.

The book will be of benefit to researchers, practitioners and graduate students engaged with optimization-based problem solving. For multiobjective optimization experts, the book is an up-to-date account of emerging and advanced topics; for others, the book indicates how the multiobjective approach can lead to fresh insights.

 

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Contents

Problem Solving EC and EMO Joshua Knowles David Corne and Kalyanmoy Deb
1
Exploiting Multiple Objectives From Problems to Solutions
29
Multiobjective Optimization and Coevolution Sevan Gregory Ficici
30
Constrained Optimization via Multiobjective Evolutionary Algorithms Efren MezuraMontes and Carlos A Coello Coello
53
Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms Lam T Bui MinhHa Nguyen Jurgen Branke and Hussein A Abbass
77
Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms Vincenzo Cutello Giuseppe ...
92
Can SingleObjective Optimization Profit from Multiobjective Optimization? Frank Neumann and Ingo Wegener
115
Implications for Interpreting the Pareto Set and for Decision Making Julia Handl and Joshua Knowles
131
Multiobjective Classification Rule Mining Hisao Ishibuchi Isao Kuwajima and Yusuke Nojima
219
Multiple Objectives in Design and Engineering
241
Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization Kalyanmoy Deb and Aravind Srinivasan
243
Melding Human and Machine Capability to Satisfy Multiple Criteria Ian C Parmee Johnson A R Abraham Azahar Machwe
263
The Study of Multiobjective Artificial Systems and Multifitness Natural Systems Amiram Moshaiov
284
Scaling up Multiobjective Optimization
305
Fitness Assignment Methods for ManyObjective Problems Evan J Hughes
307
Modeling Regularity to Improve Scalability of ModelBased Multiobjective Optimization Algorithms Yaochu Jin Aimin Zhou Qingfu Zhang Bernhar...
330

Machine Learning with Multiple Objectives
152
Multiobjective Supervised Learning Jonathan E Fieldsend and Richard M Everson
153
Reducing Bloat in GP with Multiple Objectives Stefan Bleuler Johannes Bader and Eckart Zitzler
177
A Practical Application Katya RodriguezVazquez and Peter J Fleming
201
Objective Set Compression Edwin D de Jong and Anthony Bucci
357
On Handling a Large Number of Objectives A Posteriori and During Optimization Dimo Brockhoff Dhish Kumar Saxena Kalyanmoy Deb and Eckar...
377
Index
404
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