It is necessary to understand how to universally and efficiently encode the graph node and the node context.

Why: adaptive mutations can work more efficiently if they have some information about the node. For example, reinforcement learning works taking into account the state: S_n -> A, where the state S_n is the encoded context of the node, and the action A is the most useful mutation.

There are the following thoughts:
⁃ It is worth looking towards the topic of Node Embeddings (node2vec and other approaches)
⁃ The context of the node must somehow take into account the immediate ancestors
⁃ It seems that it is important to take into account the type of node (in automl this is an operation)
⁃ The encoding of a node may involve features of the type of degree of the node and something like that

Steps:

• Research literature on node/graph encodings and embeddings
• Implement a prototype
• Then experiment: compare Contextual Bandits with simple Multi-Armed Bandits (MABs).