Multi-objective evolutionary algorithms are a class of stochastic optimization Techniques that simulate biological evolution to solve problems with multiple (and often conflicting) objectives. Advances made in the field of evolutionary multi-objective optimization (EMO) are the results of more than two decades of research, studying various topics that are unique to MO problems, such as fitness assignment, diversity preservation, balance between exploration and exploitation, elitism and archiving. However many of these studies assume that the problem is deterministic, while the EMO performance generally deteriorates in the presence of uncertainties. In certain situations, the solutions found may not even be implementable in practice. The lecture will first provide an overview of evolutionary computation and its application to multi-objective optimization. It will then discuss challenges faced in EMO research and present various EMO features and algorithms for good optimization performance. Specifically, the impact of noise uncertainties will be described and enhancements to basic EMO algorithmic design for robust optimization will be presented. The lecture will also discuss the applications of EMO techniques for solving engineering problems, such as control system design and scheduling, which often involve different competing specifications in a large and constrained search space.
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