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. |
From inside the book
Results 1-5 of 89
... presented an organized variety of MOEA topics based on fundamental principles derived from single-objective evolutionary algorithm (EA) optimization and multiobjective problem (MOP) domains. Yet, many new developments occurred in the ...
... presented with historical and algorithmic insight. Being aware of the many facets of historical multiobjective problem solving provides a foundational understanding of the discipline. Various MOEA techniques, operators, parameters and ...
... presented in Chapter 5. Also , an ex- tensive discussion of possible comparison metrics and presentation techniques are presented . This includes a brief treatment of some recent findings regard- ing the limitations of unary performance ...
... presented for each generic application and issues such as genetic operators and encodings are also briefly discussed. Chapter 8 classifies and analyzes the existing research on parallel MOEAs. The three foundational paradigms (master ...
... presented in Definition 1 con- tinues to be addressed by many search techniques including numerous evolu- tionary algorithms . Definition 1 ( General Single - Objective Optimization Problem ) : A general single - objective optimization ...
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 |