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Evolutionary algorithms for solving multi-objective problems. 2nd ed. (English) Zbl 1142.90029
Genetic Evolutionary Computation Series. New York, NY: Springer (ISBN 978-0-387-33254-3/hbk). xxi, 800 p. (2007).
The content of the book provides a general overview of the field now called evolutionary multiobjective optimization, which refers to the use of the evolutionary algorithms of any sort to solve multiobjective optimization problems. It covers also other metaheuristics that have been used to solve multiobjective optimization problems. This book should be of interest to the many disciplines that have to deal with multiobjective optimization. Each chapter is complemented by discussion questions and several ideas meant to trigger novel research paths.
Chapter 1 presents the basic terminology and nomenclature for use throughout the rest of the book. Chapter 2 provides an overview of the different multi-objective evolutionary (MOEAs) currently available. Chapter 3 discusses both coevolutionary MOEAs and hybridizations of MOEAs with local search procedures. A variety of MOEA implementations within each of these two types of approaches are presented summarized, categorized and analyzed. Chapter 4 presents a detailed developement of MOP test suites ranging from numerical functions to discrete NP-Complete problems and real-world applications. MOEA performance comparisons are presented in Chapter 5. Chapter 6 summarizes the MOEA theoretical results found in the literature. Chapter 7 attempts to group and classify the wide variety of applications found in the literature. Chapter 8 classifies and analyzes the existing research on parallel MOEAs. Chapter 9 describes the most representative research regarding the incorporation of preferences articulation into MOEAs. Chapter 10 discusses multiobjective extensions of other metaheuristics used for optimization.
The first edition was published in 2002 (see Zbl 1130.90002).

90C29 Multi-objective and goal programming
90-02 Research exposition (monographs, survey articles) pertaining to operations research and mathematical programming
92D15 Problems related to evolution
68T05 Learning and adaptive systems in artificial intelligence
90C59 Approximation methods and heuristics in mathematical programming
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