Objectives

I'm thinking seriously about the directions I'd like to pursue in the next six months or so. Some of these are straight-forward and easily testable, some are wild ideas or random shots-in-the-dark.

• Modular vs. centralized controller. Using two instances of the same evolved controller, one for each lateral side, had a dramatic effect on evolvability of biped.
• Does communication between instances help? (eg, a shared connection)
• Will a smaller grain work better? Even unintuitive approaches like an instance for each joint might be interesting.
• More complex neuron models. A recent paper suggests that this is indeed an important factor.
• Beer/Leaky neurons (CTRNNs), 1st-order ODE.
• Taga, 2nd-order ODE.
• Ekeberg, 3rd-order ODE. Reeve's 1999 thesis suggests this model is best for oscillation.
  
• A mix of different neuron models. Gut feeling that this is the most promising.
  
• Different schemes for linkage between networks. People seem fixated with changing weights of semi-static connections, but other approaches, like Gas Nets, are very interesting.
• Development/Embryogeny. Haven't looked into this much, but Bongard's work was pretty compelling, especially when combined with fluid or position-based connections/excitation transfer.
• Related, continuing with the "clump" approach to pattern generation. The echo-state network idea seems wonderfully suited to developmental approaches.
  
• Properties of locomotion space for different neural models. CTRNN-XOR space was looked at in some depth by USussex folks. Clearly need to investigate techniques for characterizing the space, esp interested in relation to neutral networks.
• Related, is cross-over really important? I'm still looking for alternative variatoin operators.