  Automatic programming, the task of generating computer programs compliant
with a specification without a human developer, is usually tackled either via
genetic programming methods based on mutation and recombination of programs, or
via neural language models. We propose a novel method that combines both
approaches using a concept of a virtual neuro-genetic programmer: using
evolutionary methods as an alternative to gradient descent for neural network
training}, or scrum team. We demonstrate its ability to provide performant and
explainable solutions for various OpenAI Gym tasks, as well as inject expert
knowledge into the otherwise data-driven search for solutions.
