Ebook: 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 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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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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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.

Content:
Front Matter....Pages I-XVI
Front Matter....Pages 1-1
Introduction: Problem Solving, EC and EMO....Pages 1-28
Front Matter....Pages 29-29
Multiobjective Optimization and Coevolution....Pages 31-52
Constrained Optimization via Multiobjective Evolutionary Algorithms....Pages 53-75
Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms....Pages 77-91
Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms....Pages 93-114
Can Single-Objective Optimization Profit from Multiobjective Optimization?....Pages 115-130
Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making....Pages 131-151
Front Matter....Pages 153-153
Multiobjective Supervised Learning....Pages 155-176
Reducing Bloat in GP with Multiple Objectives....Pages 177-200
Multiobjective GP for Human-Understandable Models: A Practical Application....Pages 201-218
Multiobjective Classification Rule Mining....Pages 219-240
Front Matter....Pages 241-241
Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization....Pages 243-262
User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria....Pages 263-283
Multi-competence Cybernetics: The Study of Multiobjective Artificial Systems and Multi-fitness Natural Systems....Pages 285-304
Front Matter....Pages 305-305
Fitness Assignment Methods for Many-Objective Problems....Pages 307-329
Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms....Pages 331-355
Objective Set Compression....Pages 357-376
On Handling a Large Number of Objectives A Posteriori and During Optimization....Pages 377-403
Back Matter....Pages 405-409

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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.

Content:
Front Matter....Pages I-XVI
Front Matter....Pages 1-1
Introduction: Problem Solving, EC and EMO....Pages 1-28
Front Matter....Pages 29-29
Multiobjective Optimization and Coevolution....Pages 31-52
Constrained Optimization via Multiobjective Evolutionary Algorithms....Pages 53-75
Tackling Dynamic Problems with Multiobjective Evolutionary Algorithms....Pages 77-91
Computational Studies of Peptide and Protein Structure Prediction Problems via Multiobjective Evolutionary Algorithms....Pages 93-114
Can Single-Objective Optimization Profit from Multiobjective Optimization?....Pages 115-130
Modes of Problem Solving with Multiple Objectives: Implications for Interpreting the Pareto Set and for Decision Making....Pages 131-151
Front Matter....Pages 153-153
Multiobjective Supervised Learning....Pages 155-176
Reducing Bloat in GP with Multiple Objectives....Pages 177-200
Multiobjective GP for Human-Understandable Models: A Practical Application....Pages 201-218
Multiobjective Classification Rule Mining....Pages 219-240
Front Matter....Pages 241-241
Innovization: Discovery of Innovative Design Principles Through Multiobjective Evolutionary Optimization....Pages 243-262
User-Centric Evolutionary Computing: Melding Human and Machine Capability to Satisfy Multiple Criteria....Pages 263-283
Multi-competence Cybernetics: The Study of Multiobjective Artificial Systems and Multi-fitness Natural Systems....Pages 285-304
Front Matter....Pages 305-305
Fitness Assignment Methods for Many-Objective Problems....Pages 307-329
Modeling Regularity to Improve Scalability of Model-Based Multiobjective Optimization Algorithms....Pages 331-355
Objective Set Compression....Pages 357-376
On Handling a Large Number of Objectives A Posteriori and During Optimization....Pages 377-403
Back Matter....Pages 405-409
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