Incremental Growth on Compositional Pattern Producing Networks Based Optimization of Biohybrid Actuators

One of the training methods of Artificial Neural Networks is Neuroevolution (NE) or the application of Evolutionary Optimization on the architecture and weights of networks to fit the target behaviour. In order to provide competitive results, three key concepts of the NE methods require more attention, i.e., the crossover operator, the niching capacity and the incremental growth of the solutions’ complexity. Here we study an appropriate implementation of the incremental growth for an application of NE on Compositional Pattern Producing Networks (CPPNs) that encode the morphologies of biohybrid actuators. The target for these actuators is to enable the efficient angular movement of a drug-delivering catheter in order to reach difficult areas in the human body. As a result, the methods presented here can be a part of a modular software pipeline that will enable the automatic design of Biohybrid Machines (BHMs) for a variety of applications. The proposed initialization with minimal complexity of these networks resulted in faster computation for the predefined computational budget in terms of number of generations, notwithstanding that the emerged champions have achieved similar fitness values with the ones that emerged from the baseline method. Here, fitness was defined as the maximum deflection of the biohybrid actuator from its initial position after 10 s of simulated time on an open-source physics simulator. Since, the implementation of niching was already employed in the existing baseline version of the methodology, future work will focus on the application of crossover operators.