I'm actually interested in the "hybrid" notion that Frederik was asking about (as opposed to the co-evolutionary solution suggested by Sean).
My work involves neural networks that have both qualitative and quantitative characteristics. That is, they have topological structure that can be evolved with the traditional GP Tree-based approach. However, the high-dimensional weighted connectivity between nodes, and parameterized transforms within the nodes, can be efficiently optimized using other traditional ANN techniques.  The way I’ve been dealing with this in the past, is to evolve networks using GP to “explore” the structural solution space, and then rely on training (evaluation) to exploit the available solution sub-space which can be done using GA, gradient descent, and so on. This is obviously computationally expensive, no matter how you dice it.
I wonder if an implementation of HyperNEAT  evolution could be added to ECJ that would efficiently blend these things in an elegant way? Has anyone out there tried this?
The robotics sensor issues Frederik mentioned, despite my lack of knowledge in that problem domain, sound very familiar to me.
Ben Stabile
-----Original Message-----
From: Frederik
Sent: Monday, November 14, 2011 11:49 AM
To: [log in to unmask]
Subject: A Mixed GA/GP individual
Dear all,
I've been experimenting with ECJ for some weeks now. I have a fellow student's
code who already succeeded to do develop robot controllers using GP individuals
and an extern simulator.
What I want to try now is to evolve some kind of indivual where the controller still
is a GP individual (subpopulation 0?) but the senors are coded into a vector of bits
or integers (subpopulation 1?) . Breeding should be between vectors or controllers
of the same individuals. Is there an easy way to do such a thing? Is there a
similar predifined parameter file for this?
Thank you in advance,
With kind regards,