Evolutionary neural architecture design for multivariate time-series forecasting remains under-explored, with most approaches relying on fixed Transformer architectures despite substantial variation across tasks and forecasting settings. This paper introduces an evolutionary neural architecture search framework for discovering task-adaptive Transformer-like models for time-series forecasting (EvoTS). Architectures are encoded using a modular genome representation that enables flexible composition of attention, feedforward, and projection components, while a repair mechanism enforces structural validity throughout the evolutionary process. This formulation allows effective exploration of a diverse architecture space without relying on hand-crafted design rules.

High-performance computing (HPC) systems are difficult to use due to complex job scheduling, resource selection, and limited feedback on job failures. This paper presents Inkly, a data-driven HPC assistant with job intelligence that augments user workflows with insights derived from historical Slurm job data. Inkly ingests job records via sacct, stores them in a SQLite database, and computes aggregate metrics such as partition success rates, CPU and memory usage patterns, and failure distributions. These metrics are added to the prompts to guide users toward more effective job configurations. The system enforces safety through prompt filtering, command guardrails, and containerized execution using Apptainer.

Self-adaptive systems frequently use tactics to perform adaptations. Tactic examples include the implementation of additional security measures when an intrusion is detected, or activating a cooling mechanism when temperature thresholds are surpassed. Tactic volatility occurs in real-world systems and is defined as variable behavior in the attributes of a tactic, such as its latency or cost. A system's inability to effectively account for tactic volatility adversely impacts its efficiency and resiliency against the dynamics of real-world environments. To enable systems' efficiency against tactic volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing evolved Recurrent Neural Networks (eRNN) to provide accurate tactic predictions. TVA-E is also the first known process to take advantage of uncertainty reduction tactics to provide additional information to the decision-making process and reduce uncertainty. TVA-E easily integrates into popular adaptation processes enabling it to immediately benefit a large number of existing self-adaptive systems. 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. The developed dataset and tool can be found at https://tacticvolatility.github.io/

Transfer learning entails taking an artificial neural network (ANN) that is trained on a source dataset and adapting it to a new target dataset. While this has been shown to be quite powerful, its use has generally been restricted by architectural constraints. Previously, in order to reuse and adapt an ANN's internal weights and structure, the underlying topology of the ANN being transferred across tasks must remain mostly the same while a new output layer is attached, discarding the old output layer's weights. This work introduces network-aware adaptive structure transfer learning (N-ASTL), an advancement over prior efforts to remove this restriction. N-ASTL utilizes statistical information related to the source network's topology and weight distribution in order to inform how new input and output neurons are to be integrated into the existing structure. Results show improvements over prior state-of-the-art, including the ability to transfer in challenging real-world datasets not previously possible and improved generalization over RNNs without transfer.

This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Δ-RNN, GRU, LSTM, MGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale, real world time series data from the aviation and power industries. These data sets consist of very long time series (thousands of readings), each with a large number of potentially correlated and dependent parameters. Four different parameters were selected for prediction and EXAMM runs were performed using each memory cell type alone, each cell type and simple neurons, and with all possible memory cell types and simple neurons. Evolved RNN performance was measured using repeated k-fold cross validation, resulting in 2420 EXAMM runs which evolved 4, 840, 000 RNNs in ~24,200 CPU hours on a high performance computing cluster. Generalization of the evolved RNNs was examined statistically, providing findings that can help refine the design of RNN memory cells as well as inform future neuro-evolution algorithms.

This work examines the use of ant colony optimization (ACO) to improve long short-term memory (LSTM) recurrent neural networks (RNNs) by refining their cellular structure. The evolved networks were trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. Results were obtained using MPI (Message Passing Interface) on a high performance computing (HPC) cluster, which evolved 1000 different LSTM cell structures using 208 cores over 5 days. The new evolved LSTM cells showed an improvement in prediction accuracy of 1.37%, reducing the mean prediction error from 6.38% to 5.01% when predicting excessive engine vibrations 10 seconds in the future, while at the same time dramatically reducing the number of trainable weights from 21,170 to 11,650. The ACO optimized LSTM also performed significantly better than traditional Nonlinear Output Error (NOE), Nonlinear AutoRegression with eXogenous (NARX) inputs, and Nonlinear Box-Jenkins (NBJ) models, which only reached error rates of 11.45%, 8.47% and 9.77%, respectively. The ACO algorithm employed could be utilized to optimize LSTM RNNs for any time series data prediction task.

This paper examines building viable Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) neurons to predict aircraft engine vibrations. The model is trained on a large database of flight data records obtained from an airline containing flights that suffered from excessive vibration. RNNs can provide a more generalizable and robust method for prediction over analytical calculations of engine vibration, as analytical calculations must be solved iteratively based on specific empirical engine parameters, and this database contains multiple types of engines. Further, LSTM RNNs provide a “memory” of the contribution of previous time series data which can further improve predictions of future vibration values. LSTM RNNs were used over traditional RNNs, as those suffer from vanishing/exploding gradients when trained with back propagation. The study managed to predict vibration values for 5, 10 and 20 seconds in the future, with 3.3%, 5.51% and 10.19% mean absolute error, respectively. These neural networks provide a promising means for the future development of warning systems so that suitable actions can be taken before the occurrence of excess vibration to avoid unfavorable situations during flight.