Minimal optimal topologies for invariant higher-order neural architectures using genetic algorithms

Higher-order neural networks (HONNs) are successful in performing position, rotation and scale (PRSI) recognition. A major limitation of these networks is the combinatorial explosion of the higher-order terms, which increases the complexity of the network architecture. This work proposes a genetic optimisation scheme for determining the minimal optimal topology of a network for automated inspection of industrial parts.