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Continuous Ant-Based Neural Topology Search
This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), which utilizes synthetic ants that move over a continuous search space based on the density and distribution of pheromones, is strongly inspired by how ants move in the real world.
An Experimental Study of Weight Initialization and Weight Inheritance Effects on Neuroevolution
Weight initialization is critical in being able to successfully train artificial neural networks (ANNs), and even more so for recurrent neural networks (RNNs) which can easily suffer from vanishing and exploding gradients.
Neuroevolutionary Transfer Learning of Deep Recurrent Neural Networks through Network-Aware Adaptation
Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset.
Improving Neuroevolution Using Island Extinction and Repopulation
This island based strategy is additionally compared to NEAT's (NeuroEvolution of Augmenting Topologies) speciation strategy.
