Evolutionary Algorithms for Solving Multi-Objective ProblemsSolving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems. This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems, including test suites with associated performance based on a variety of appropriate metrics, as well as serial and parallel algorithm implementations. |
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... better search techniques. The explosion of successful MOEA applications continues to be reported in the literature. Statistical testing methods for evaluating results now offers improved analysis of comparative techniques, innovative ...
... better . " The Pareto optimal set contains those better solutions . Identifying a set of Pareto optimal solutions is thus key for a DM's selection of a " compromise " solution satisfying the objectives as " best " possible . Of course ...
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Contents
1 | |
57 | |
Further Explorations 123 | 122 |
MOEA Local Search and Coevolution | 131 |
Further Explorations | 171 |
Further Explorations | 229 |
Further Explorations | 277 |
MOEA Theory and Issues | 283 |
Further Explorations 335 | 334 |
Further Explorations 437 | 436 |
Further Explorations | 509 |
Further Explorations | 541 |
Further Explorations | 617 |
References | 627 |
Index | 761 |