The role of computational intelligence in experimental design: A literature review

Experimental design (DOE) is a well-developed methodology that has been frequently adopted for different purposes in a wide range of fields such as control theory, optimization, and intelligent decision making. The main objective of DOE is to best select experiments to estimate a set of parameters while consuming as little resources as possible. The enrichment of literature on computational intelligence has supported DOE to extend its sphere of influence in the past two decades. Specifically, the most significant progress has been observed in the area of optimal experimentation, which deals with the calculation of the best scheme of measurements so that the information provided by the data collected is maximized. Nevertheless, determining the design that captures the true relationship between the response and control variables is the most fundamental objective. When deciding whether a design is better (or worse) than another one, usually a criterion is utilized to make an objective distinction. There is a wide range of optimality criteria available in the literature that has been proposed to solve theoretical or practical problems stemming from the challenging nature of optimal experimentation. This study focuses on the most recent applications of DOE related to heuristic optimization, fuzzy approach, and artificial intelligence with a special emphasis on the optimal experimental design and optimality criteria.