The wide variety of parallel and distributed versions of EAs make them an ideal candidate for use with HPC systems.

Consequently, EAs have gathered considerable attention for their ability to accelerate finding solutions for a variety of computationally expensive problem domains, including reinforcement learning, neural architecture search, and model calibration for complex simulations.

However, use of HPC resources adds an implicit secondary objective of ensuring those resources are efficiently used. This means that practitioners have to make decisions regarding evolutionary algorithms tailored to maximumize HPC resource use, as well as associated software and hardware support. New EA-oriented HPC benchmarks might also be needed to guide practitioners in making those decisions.

We are looking for papers on the following sub-topics to facilitate discussion:

algorithmic

What novel EA variants best exploit HPC resources?

benchmarks

Are there HPC specific measures for EA performance?

hardware

Can we improve use of HPC hardware, such as GPUs?

software

What EA software, or software development practices, best leverage HPC capabilities?

---