System and method for efficient evolution of deep convolutional neural networks using filter-wise recombination and propagated mutations

A method of training a plurality of convolutional neural networks (CNNs) using a genetic evolutionary model, each CNN comprising a hierarchy of layers, each layer comprising one or more channels, each channel comprising a plurality of artificial neurons, each CNN further comprising a plurality of filters, each filter representing a group of a plurality of weights of connections between neurons in a pair of channels in respective adjacent layers of the hierarchy, the method comprising:

• storing a plurality of artificial chromosomes representing the weights of the neuron connections of the plurality of respective CNNs;
• mutating a plurality of weights of one of the plurality of chromosomes to form a new chromosome by updating the plurality of weights based on errors computed for the connected neurons, the errors of the neurons computed recursively from the final output layer based on the expected values of a training dataset and propagating those error corrections backward incrementally to lower layers in the CNN to only neurons in the CNN that are connected by weights that are randomly selected to be mutated;
• updating the plurality of artificial chromosomes by adding the new chromosomes and removing at least some of the non-recombined chromosomes;
• and
• iteratively repeating said updating until the updated plurality of artificial chromosomes represent weights of CNNs that have a convergent error.

A system for training a plurality of convolutional neural networks (CNNs) using a genetic evolutionary model, each CNN comprising a hierarchy of layers, each layer comprising one or more channels, each channel comprising a plurality of artificial neurons, each CNN further comprising a plurality of filters, each filter representing a group of a plurality of weights of connections between neurons in a pair of channels in respective adjacent layers of the hierarchy, the system comprising:

• one or more memories to store a plurality of artificial chromosomes representing the weights of the neuron connections of the plurality of respective CNNs;
• and
• one or more processors configured to:
• recombine a plurality of pairs of the chromosomes to generate, for each pair, a new chromosome that includes a different set of weights than in either chromosome of the pair, wherein recombining comprises selecting entire filters as inseparable groups of a plurality of weights from each of the pair of chromosomes by traversing the length of the pair of chromosomes, progressing filter-by-filter, switching back and forth at random between selecting a filter from a first chromosome or a second chromosome in the pair,
• update the plurality of artificial chromosomes by adding the new chromosomes and removing at least some of the non-recombined chromosomes, and
• iteratively repeat said update until the updated plurality of artificial chromosomes represent weights of CNNs that have a convergent error.

A system for training a plurality of convolutional neural networks (CNNs) using a genetic evolutionary model, each CNN comprising a hierarchy of layers, each layer comprising one or more channels, each channel comprising a plurality of artificial neurons, each CNN further comprising a plurality of filters, each filter representing a group of a plurality of weights of connections between neurons in a pair of channels in respective adjacent layers of the hierarchy, the system comprising:

• one or more memories to store a plurality of artificial chromosomes representing the weights of the neuron connections of the plurality of respective CNNs;
• and
• one or more processors configured to:
• mutate a plurality of weights of one of the plurality of chromosomes to form a new chromosome by updating the plurality of weights based on errors computed for the connected neurons, the errors of the neurons computed recursively from the final output layer based on the expected values of a training dataset and propagating those error corrections backward incrementally to lower layers in the CNN to only neurons in the CNN that are connected by weights that are randomly selected to be mutated,
• update the plurality of artificial chromosomes by adding the new chromosomes and removing at least some of the non-recombined chromosomes, and
• iteratively repeat said update until the updated plurality of artificial chromosomes represent weights of CNNs that have a convergent error.