  Logic locking protects the integrity of hardware designs throughout the
integrated circuit supply chain. However, recent machine learning (ML)-based
attacks have challenged its fundamental security, initiating the requirement
for the design of learning-resilient locking policies. A promising ML-resilient
locking mechanism hides within multiplexer-based locking. Nevertheless, recent
attacks have successfully breached these state-of-the-art locking schemes,
making it ever more complex to manually design policies that are resilient to
all existing attacks. In this project, for the first time, we propose the
automatic design exploration of logic locking with evolutionary computation
(EC) -- a set of versatile black-box optimization heuristics inspired by
evolutionary mechanisms. The project will evaluate the performance of
EC-designed logic locking against various types of attacks, starting with the
latest ML-based link prediction. Additionally, the project will provide
guidelines and best practices for using EC-based logic locking in practical
applications.
