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Found 8 papers, 1 papers with code
Colony-Enhanced Recurrent Neural Architecture Search: Collaborative Ant-Based Optimization
Crafting neural network architectures manually is a formidable challenge often leading to suboptimal and inefficient structures.
Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search
Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO).
Addressing Tactic Volatility in Self-Adaptive Systems Using Evolved Recurrent Neural Networks and Uncertainty Reduction Tactics
Simulations using 52, 106 tactic records demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E represents an improvement over existing state-of-the-art processes in accounting for tactic volatility, and III) Uncertainty reduction tactics are beneficial in accounting for tactic volatility.
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.
Optimizing Long Short-Term Memory Recurrent Neural Networks Using Ant Colony Optimization to Predict Turbine Engine Vibration
This research improves the performance of the most effective LSTM network design proposed in the previous work by using a promising neuroevolution method based on ant colony optimization (ACO) to develop and enhance the LSTM cell structure of the network.
