Abstract:

In experimental optimization the quality of candidate solutions can be 
evaluated only by means of an experiment in the real-world. These 
experiments are often time-consuming and/or expensive, and are typically 
limited to several dozens or hundreds of trials. High-dimensional 
problems (i.e., at least 80 search variables) cannot be efficiently 
handled by classical convex optimizers, and thus require an alternative 
treatment. Derandomized Evolution Strategies (DES) are powerful 
bio-inspired search methods, originating from Evolutionary Algorithms, 
that incorporate statistical learning for efficient derandomized search. 
This talk will focus on the theory behind state-of-the-art DES, as well 
as on their application to experimental optimization. Especially, it 
will discuss optimization efficiency, attainment of robust solutions, 
exploration of the actual search landscape, and the generalization into 
Pareto optimization of multiple objectives. Special emphasis will be put 
on a particular experimental platform employing DES at present times, 
namely Quantum Control experiments. The Quantum Control (QC) field aims 
at altering the course of quantum dynamics phenomena for specific target 
realizations, by means of closed-loop, adaptively learned laser pulses. 
The optimization task of QC experiments typically poses many algorithmic 
challenges, e.g., high-dimensionality, noise, constraints handling, and 
thus offers a rich domain for the development and application of 
specialized optimizers. Toward that end, the computational aspects of 
several real-world laboratory optimization case-studies will be presented.

**This talk will be self-contained, and will target the general audience 
of CS. It will not require any specialized background in Quantum 
Mechanics nor in Optimization.