Multiobjective problems involve several competing measures of solution quality. Multiobjective evolutionary algorithms (MOEAs) and multiobjective problem solving have become important topics of research in the evolutionary computation community. This text examines 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 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. Unlike comparable texts, this text does not focus on algorithms nor specific applications, but rather on the concepts and processes involved in solving problems using a multiobjective approach. Overall, this text evaluates the utility of the multiobjective approach and discusses alternative problem formulations. Moreover, it examines solution selection and decision-making and shows how problem formulation affects the search process. Readers will discover how the multiobjective approach can lead to fresh insights.
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