This tutorial addresses applications of evolutionary algorithms to optimization tasks where the function evaluation cannot be done through a computer simulation, but requires the execution of an experiment in the real world (i.e., cosmetics, detergents, wind tunnel experiments, taste experiments, to mention a few).   The use of EAs for experimental optimization is placed in its historical context with an overview of the landmark studies in this area carried out in the 1960s at the Technical University of Berlin. Statistical design of experiments (DoE) methods from the late 50s are also reviewed, and it is shown how relatives of these approaches are converging in modern sequential DoE/EA hybrid methods.   The main characteristics of experimental optimization work, in comparison to optimization of simulated systems, are discussed, and practical guidelines for real-world experiments with EAs are given. For example, experimental problems can constrain the evolution due to overhead considerations, interruptions, changes of variables, and population sizes that are determined by the experimental platform.   A series of modern-day case studies shows the persistence of experimental optimization problems today. These cover experimental quantum control, real DNA and RNA evolution, combinatorial drug discovery, coffee and chocolate processing, and others. These applications can push EA methods outside of their normal operating envelope, and raise research questions in a number of different areas ranging across constrained EAs, multiobjective EAs, robust and reliable methods for noisy problems, and metamodeling methods for expensive cost functions.
['Thomas Bäck', 'Joshua D. Knowles', 'Ofer M. Shir']
Experimental optimization by evolutionary algorithms