Evolutionary Multiobjective Optimization (EMO)

Evolutionary Multiobjective Optimization (EMO) is the commonly used term for the study and development of evolutionary algorithms to tackle optimization problems with at least two conflicting optimization objectives. The first methods were proposed in the 1980s, and the field has gradually emerged as one of the most innovative and popular areas of evolutionary computation, with a reach extending far beyond its niche beginnings.Today, EMO methods are frequently developed and adopted by researchers from other areas of optimization and decision making, and are put to use in a wealth of applications.This tutorial will be a fresh look at the current state of EMO suitable for those new to the field and those who are experienced but wish to keep up-to-date with a selected tour of the latest ideas, theory, and applications. We will begin with a gentle introduction to the fundamental ideas, but will neglect a comprehensive history in order to spend more time on the most surprising and most secure results, and interesting research lines that still need further deep exploration.1. Why multiobjective? There are several motivating reasons often neglected2. Why evolutionary? We will re-examine the usual population-based justification3. Elitism and archiving, from basics to the latest synthesis of theoretical results4. NSGA-II: reflections on a behemoth, and new theoretical results5. Performance assessment and benchmarking best practices6. How many objectives? Why decreasing and increasing number of objectives can both work (surveying objective reduction and multi-objectivization)7. Decomposition and cooperative problem solving8. When and how to include the elusive decision maker (DM), including visualization, and replacing the human decision maker with machines9. Asynchronous EMO methods (for objectives of differing latency)10. Automatic design and tuning of EMO algorithmsThe tutorial will not include a comprehensive survey of applications, but selected interesting applications will serve to reflect on the challenges in the use of EMO in practice.