AN EXCLUSIVE TECHNOLOGY
THE IMPORTANCE OF TOPOLOGY
TOWARDS ARTIFICIAL INTELLIGENCES GENERATED BY AI
RAISE: DEEP-NEUROEVOLUTION APPLIED
RAISE is a framework for unconstrained topology search based on DXNN, a neuroevolutive method described by Dr. Sher (3). The system can start from a simple layer to build a network adapted to a specific problem, but it can also start from a 'state of the art' model and further improve it.
A deep-neuroevolution algorithm optimizes a neural network by an evolutionary method: the characteristics of the network (its synaptic weights and topology) form its genotype which is used to generate a population of new neural networks whose genotypes will be more or less different, “mutant” versions. Only the best performing mutant models will be kept to create the next generation.
One of the strong characteristics of deep neuroevolution is that the evolved models are no longer constrained to rigid feedforward or rigid model structures; instead, we are able to evolve any topology, from any type of layers, connected in the way that best fits the particular problem domain.
RAISE uses optimized yet unconstrained topological search. There are no constraints on the topology that can be evolved, which greatly expands the type of problem domains we can solve, and the level of performance we can reach.
- RAISE simultaneously uses gradient descent to optimize the parameters of the model (local search), and the neuroevolutive method to optimize the topology (global search).
- RAISE simultaneously uses gradient descent for a 'local' optimum and the neuroevolutive method to optimize the topology.
- Finally, RAISE can also seek to reduce the size of the network for lower memory footprint, or optimize the speed of inference, without sacrificing performance. Our evolved deep networks are generally of the smallest possible size which allows them to run on embedded systems, even the IOT.