Can genomes be heterogeneous and express entities with heterogeneous elements?

Question

I never took a formal GA course, so this question might be vague: I'm trying to see whether I'm approaching this problem well.

Usually a genome is represented as a sequence of homogeneous elements, such as binary numbers, logic gates, elementary functions, etc., which can then be assembled into a homogeneous structure like a syntax-tree for a computer program or a 3D object or whatever.

My problem involves evolving a graph of components, lets say X, Y and Z: the graph can have N nodes and each node is an instance of either X, Y or Z. Encoding such a graph structure in a genome is rather straightforward, however, I also need to attach additional information for what X, Y and Z do themselves--which is actually the main object of the GA.

So it seems like my genome should code for a heterogeneous entity: an entity which is composed both of a structure graph and a functionality specification. It is not impossible to subsume the elements (genes) which code for the structure and those that code for functionality under a single parent "gene", and then simply separate them when the entity is being assembled, but this doesn't feel like the right approach.

Is this a common problem in GA? Am I supposed to find a "lower-level" representation / genome encoding in this situation? What are the relevant considerations?

Answer 1

Yes you can do that with GA, but strictly speaking you will be using Genetic Programming (GP) as opposed of Genetic Algorithms. GP is considered a special case of GA where the genome representation is heterogenous. This means your individual is a "computer program" instead of just "raw data" look here and here. This means you can really get creative on what this "computer program" means, how to represent it and handle it.

Regarding the additional information, it should be fine as long as all your genetic operators consider this representation. For instance, your crossover. It could be prepared to exchange half of the tree and half of the additional information of the parents. If for some reason the additional information cannot be divided, your crossover may decide to clone it from one of the parents.

The main disadvantage of this highly tuned approach is that you probably can't use high level GA/GP frameworks out there (I'm just assuming, I don't know much about them).