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Surrogate-assisted hyper-parameter search for portfolio optimisation: multi-period considerations
Portfolio management is a multi-period multi-objective optimisation problem subject to various constraints. However, portfolio management is treated...
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Hyper-parameter Tuning
Hyper-parameters can be loosely defined as those parameters that are not changed during the training process. For example, number of layers in a...
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Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach
Hyper-parameter tuning of machine learning models has become a crucial task in achieving optimal results in terms of performance. Several researchers...
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Improving predictive performance in e-learning through hybrid 2-tier feature selection and hyper parameter-optimized 3-tier ensemble modeling
The paper presents a new feature selection technique developed in detail here to address improved prediction accuracy not only for the...
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Calculus and Optimisation for Machine Learning
This chapter delves into the fundamental concepts of calculus and optimisation related to machine learning, offering both theoretical insights and...
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Bayesian Optimisation of Large-scale Photonic Reservoir Computers
Reservoir computing is a growing paradigm for simplified training of recurrent neural networks, with a high potential for hardware implementations....
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Continual Model-Based Reinforcement Learning for Data Efficient Wireless Network Optimisation
We present a method that addresses the pain point of long lead-time required to deploy cell-level parameter optimisation policies to new wireless...
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Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network
The Internet of Things (IoT) is an indispensable part of the healthcare system since it creates a link between the doctor and the patient for remote...
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Online learning of variable ordering heuristics for constraint optimisation problems
Solvers for constraint optimisation problems exploit variable and value ordering heuristics. Numerous expert-designed heuristics exist, while recent...
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Surrogate-assisted evolutionary multi-objective optimisation applied to a pressure swing adsorption system
The complexity of chemical plant systems (CPS) makes optimising their design and operation challenging tasks. This complexity also results in...
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A survey, taxonomy and progress evaluation of three decades of swarm optimisation
While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In...
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Semi-parametric Approach to Random Forests for High-Dimensional Bayesian Optimisation
Calibration of simulation models and hyperparameter optimisation of machine learning and deep learning methods are computationally demanding...
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An towards efficient optimal recurrent neural network-based brian tumour classification using cat and rat swarm (CARS) optimisation
A brain tumour is a lump that forms in the brain as abnormal cells multiply and spread there. The intricacy of brain tissues makes it difficult and...
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On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems
The complexity of Multi-Objective (MO) continuous optimisation problems arises from a combination of different characteristics, such as the level of...
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Deep spatial-temporal bi-directional residual optimisation based on tensor decomposition for traffic data imputation on urban road network
The capacity of fully exploiting underlying spatial-temporal dependencies holds the key for missing traffic data imputation, however, previous...
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An Analysis on Hybrid Brain Storm Optimisation Algorithms
Optimisation can be described as the process of finding optimal values for the variables of a given problem in order to minimise or maximise one or...
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A Continuous Optimisation Benchmark Suite from Neural Network Regression
Designing optimisation algorithms that perform well in general requires experimentation on a range of diverse problems. Training neural networks is...
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Heterogeneous Heuristic Optimisation and Scheduling for First-Order Theorem Proving
Good heuristics are essential for successful proof search in first-order automated theorem proving. As a result, state-of-the-art theorem provers...
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Scaling up stochastic gradient descent for non-convex optimisation
Stochastic gradient descent (SGD) is a widely adopted iterative method for optimizing differentiable objective functions. In this paper, we propose...
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Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation
Hybrid methods that combine learning-based features with conventional optimisation have become popular for medical image registration. The ConvexAdam...