- Full Description
Solving 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.
- Table of Contents
Table of Contents
- Basic Concepts.
- Evolutionary Algorithm MOP Approaches.
- MOEA Test Suites.
- MOEA Testing and Analysis.
- MOEA Theory and Issues.
- MOEA Parallelization.
- Criteria Decision Making.
- Special Topics.
- Appendix A: MOEA Classification and Technique Analysis.
- Appendix B: MOPs in the Literature.
- Appendix C: Ptrue and PFtrue for Selected Numberic MOPs.
- Appendix D: Ptrue and PFtrue for Side
- constrained MOPs.
- Appendix E: MOEA Software Availability.
- Appendix F: MOEA
- Related Information.
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