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 90
... metrics and presentation techniques. The selection of key algorithmic parameter values (population size, crossover and mutation rates, etc.) is emphasized. A limited set of MOEA results are related to the design and analysis of ...
... 1 Selection of MOEA Comparison Measures 245 5.4.2 Generic Attainment Function 245 5.4.3 Dominance Relations 250 5.4.4 Primary Quality Indicators 254 5.4.5 Other MOEA Quality Indicators 5.4.6 MOEA Experimental Metrics Summary Contents XVII.
... Metrics Summary 5.5 MOEA Statistical Testing Approaches 5.5.1 Statistical Testing Techniques . . 5.5.2 Non - Parametric Statistics ( Analysis of Variance ) 5.5.3 Methods for Presentation of MOEA Results 5.5.4 Visualization of Test ...
... 4 pMOEA Test Function Issues 480 8.5.5 pMOEA Metric / Parameter Issues 484 8.6 PMOEA Development Issues . 488 8.6.1 pMOEA Creation Options . 490 9 8.6.2 Master - Slave Implementation Issues 8.6.3 Island Implementation Contents XIX.
... metrics and presentation techniques are presented . This includes a brief treatment of some recent findings regard- ing the limitations of unary performance metrics . Results are related to the design and analysis of efficient and ...
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 |