Section: Application Domains

Applications

Applications of black-box algorithms occur in various domains. Industry but also researchers in other academic domains have therefore a great need to apply black-box algorithms on a daily basis. We see this as a great source of motivation to design better methods. Applications not only allow us to backup our methods and understand what are the relevant features to solve a real-world problem but also help to identify novel difficulties or set priorities in terms of algorithm design.

We are currently dealing with concrete applications related to three industrial collaborations:

• With EDF R&D through the design and placement of bi-facial photovoltaic panels for the postdoc of Asma Atamna funded by the PGMO project NumBER.

• With Thales for the PhD thesis of Konstantinos Varelas (DGA-CIFRE thesis) related to applications in the defense domain.

• With Storengy, a subsidiary of Engie specialized in gas storage, for the PhD thesis of Cheikh Touré.

Another type of application we want to focus on comes from reinforcement learning. The problems addressed in [27] seem to be particularly suited for large-scale variants of CMA-ES.

When dealing with single applications, the results observed are difficult to generalize: typically not many methods are tested on a single application as tests are often time consuming and performed in restrictive settings. Yet, if one circumvent the problem of confidentiality of data and of criticality for companies to publish their applications, real-world problems could become benchmarks as any other analytical function. This would allow to test wider ranges of methods on the problems and to find out whether analytical benchmarks properly capture real-world problem difficulties. We will thus seek to incorporate real-world problems within our COCO platform. This is a recurrent demand by researchers in optimization.