  Today, machine learning tools, particularly artificial neural networks, have
become crucial for diverse applications. However, current digital computing
tools to train and deploy artificial neural networks often struggle with
massive data sizes and high power consumptions. Optical computing provides
inherent parallelism and perform fundamental operations with passive optical
components. However, most of the optical computing platforms suffer from
relatively low accuracies for machine learning tasks due to fixed connections
while avoiding complex and sensitive techniques. Here, we demonstrate a
genetically programmable yet simple optical neural network to achieve high
performances with optical random projection. By genetically programming the
orientation of the scattering medium which acts as a random projection kernel
and only using 1% of the search space, our novel technique finds an optimum
kernel and improves its initial test accuracies 7-22% for various machine
learning tasks. Our optical computing method presents a promising approach to
achieve high performance in optical neural networks with a simple and scalable
design.
