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Self Hyper-parameter Tuning for Stream Recommendation Algorithms
E-commerce platforms explore the interaction between users and digital content – user generated streams of events – to build and maintain dynamic...
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An Evaluation of Self-supervised Learning for Portfolio Diversification
Recently self-supervised learning (SSL) has achieved impressive performance in computer vision (CV) and natural language processing (NLP) tasks, and...
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Application of neural networks to predict indoor air temperature in a building with artificial ventilation: impact of early stop**
Indoor air temperature prediction can facilitate energy-saving actions without compromising the indoor thermal comfort of occupants. The aim of this...
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Data-driven Dimensional Expression Generation via Encapsulated Variational Auto-Encoders
Concerning facial expression generation, relying on the sheer volume of training data, recent advances on generative models allow high-quality...
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Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review
The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of...
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A Diversity-Based Synthetic Oversampling Using Clustering for Handling Extreme Imbalance
Imbalanced data are typically observed in many real-life classification problems. However, mainstream machine learning algorithms are mostly designed...
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Empirical Investigation of MOEAs for Multi-objective Design of Experiments
Many machine learning algorithms require the use of good quality experimental designs to maximise the information available to the model. Various...
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A Systematic Comparison on Prevailing Intrusion Detection Models
Modern vehicles have become connected via On-Board Units (OBUs) involving many complex embedded and networked devices with steadily increasing...
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Automated machine learning: past, present and future
Automated machine learning (AutoML) is a young research area aiming at making high-performance machine learning techniques accessible to a broad set...
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Hyperparameter Optimization of Deep Learning Models for EEG-Based Vigilance Detection
ElectroEncephaloGraphy (EEG) signals have a nonlinear and complex nature and require the design of sophisticated methods for their analysis. Thus,...
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Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data
The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry,...
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Evolutionary Reduction of the Laser Noise Impact on Quantum Gates
As the size of quantum hardware progressively increases, the conjectured computational advantages of quantum technologies tend to be threatened by...
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Elitism-Based Genetic Algorithm Hyper-heuristic for Solving Real-Life Surgical Scheduling Problem
Hyper-heuristic was designed to automate the development of computational search methodologies. Although it has effectively handled a variety of...
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Uncertainty Estimation in Liver Tumor Segmentation Using the Posterior Bootstrap
Deep learning-based medical image segmentation is widely used and has achieved the state-of-the-art segmentation performance, in which nnU-Net is a...
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An Evolutionary Deep Learning Approach for Efficient Quantum Algorithms Transpilation
Gate-based quantum computation describes algorithms as quantum circuits. These can be seen as a set of quantum gates acting on a set of qubits. To be...
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Automatic model training under restrictive time constraints
We develop a hyperparameter optimisation algorithm, Automated Budget Constrained Training, which balances the quality of a model with the...
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A Comparative Analysis of Evolutionary Adversarial One-Pixel Attacks
Adversarial attacks pose significant challenges to the robustness of machine learning models. This paper explores the one-pixel attacks in image...
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Evolving ensembles of heuristics for the travelling salesman problem
The Travelling Salesman Problem (TSP) is a well-known optimisation problem that has been widely studied over the last century. As a result, a variety...
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Constructing generative logical models for optimisation problems using domain knowledge
In this paper we seek to identify data instances with a low value of some objective (or cost) function. Normally posed as optimisation problems, our...
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Cooperative Coevolutionary Genetic Programming Hyper-Heuristic for Budget Constrained Dynamic Multi-workflow Scheduling in Cloud Computing
Dynamic Multi-workflow Scheduling (DMWS) in cloud computing is a well-known combinatorial optimisation problem. It is a great challenge to tackle...