Introducing user preference using Desirability Functions in Multi-Objective Evolutionary Optimisation of noisy processes

Multi-objective evolutionary algorithms (MOEAs) are generally designed to find a well spread Pareto-front approximation. Often, only a small section of this front may be of practical interest. Desirability functions (DFs) are able to describe user preferences intuitively. Furthermore, DFs can be attached to any fitness function easily. This way, desirability functions can help in guiding MOEAs without introducing additional restrictions or changes to the algorithm. The application of noisy fitness functions is not straight forward but relevant to many real-world problems. Therefore, a variant of Harrington´s one-sided desirability function using expectations is introduced which takes noise into account. A deterministic strategy as well as the XSGA-II are used in combination with DF to solve a noisy Binh problem and a noisy cost estimation problem for turning processes.