Research indirect genotype encoding of graph phenotype

This is based on this [GECCO'22 paper](https://arxiv.org/pdf/2202.09679.pdf). At least one of the authors is a quite known researcher in evolutionary field, so likely he knows what he talks about.

The idea of the paper is that indirect rich encodings of individuals (i.e. with genotypes that are much richer that phenotypes) can lead to *exponential* improvements in evolutionary algo. As paper says, this is because with rich genotypes (e.g. evolving strings using neural network genotype) evolution "accumulates" valuable information in the genom, while for simple direct genotypes (e.g. encoding strings as strings) evolution can't and behaves more like an advanced random search.

In GOLEM currently we have direct encoding (graph phenotype is represented directly with graph genotype). The hypothesis is that with richer genotype (encoding graph with generative neural network?...) evolution can be much more effective. Possibly that would help in resolving #69, where evolution converges. This example would also server as experiment in testing the hypothesis.

Formal details and requirements for such a genotype are in the paper. Examples there are worth reading.