| Preface | 6 |
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| Contents | 8 |
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| List of Contributors | 11 |
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| Introduction: Problem Solving, EC and EMO | 15 |
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| Exploiting Multiple Objectives: From Problems to Solutions | 43 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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