Fabio Caraffini

Swansea University | SWAN · Department of Computer Science

In the last three decades, the field of computational intelligence has seen a profusion of population-based metaheuristics applied to a variety of problems, where they achieved state-of-the-art results. This remarkable growth has been fuelled and, to some extent, exacerbated by various sources of inspiration and working philosophies, which have bee...

Recently, new technologies have been developed that allow physical and virtual space to converge. We reviewed a range of innovative technologies that enable immersive and 3D interaction, which we believe are of particular interest to apply Universal Design for Learning (UDL) principles in teaching and learning practices, with a particular interest...

The financial impacts of the consequences of climate change on organisations are not always clear. Therefore, for many organisations, assessing the potential impact of climate risk remains a challenge. This piece of research is geared towards selecting the best artificial intelligence technique for modelling and forecasting the operational costs re...

Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms. The toolbox can be used to identify biases in existing algorithms, as well as to test for bi...

Presentation for the paper: Mădălina-Andreea Mitran, Anna Kononova, Fabio Caraffini, and Daniela Zaharie. 2023. Patterns of Convergence and Bound Constraint Violation in Differential Evolution on SBOX-COST Benchmarking Suite. In Proceedings of the Companion Conference on Genetic and Evolutionary Computation (GECCO '23 Companion). Association for C...

This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the number of cases when a BCHM is activated. The empirical analysis is conducted on the SBOX-COST benc...

We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple bound constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or invest...

New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an extension of a preexisting algorithm. Although these contributions are often compared to the base algorithm, it is challenging to make fair comparisons between larger set...

Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algorithms. The toolbox can be used to identify biases in existing algorithms, as well as to test for bi...

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without performing structure elucidation. This can help to reduce the time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpre...

In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlenbeck process with reversion to the mean. We also propose a Stochastic Flexible - Adaptive Neura...

Swarm and evolutionary computation (SEC) [...]

This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by a human engineer, or at the very least a process for matching must be meaningfully interpreted by on...

There is a large volume of literature in international business on multinationality. There is an equally large volume of literature in finance on stock price crash risk. However, very few studies have attempted to provide a link between these two research areas. Using an unbalanced panel data consisting of 473 multinational corporations (MNCs) publ...

The Covid-19 pandemic has led to the adoption of face masks in physical teaching spaces across the world. This has in-turn presented a number of challenges for practitioners in the face-to-face delivery of content and in effectively engaging learners in practical settings, where face coverings are an ongoing requirement. Being unable to identify th...

The future of education lies in the ability to develop technologies which integrate seamless artificial intelligence (AI) components into the educational process, in order to deliver a personalized service which is dynamically tailored to the learner's characteristics, abilities, and needs [...]

We demonstrate that particle swarm optimisation (PSO) can be used to solve a variety of problems arising during operation of a digital inspection microscope. This is a use case for the feasibility of heuristics in a real-world product. We show solutions to four measurement problems, all based on PSO. This allows for a compact software implementatio...

The following work presents a software solution capable of designing general meal plans which approach an optimal match of nutritional characteristics submitted by the user. A thorough review of existing literature indicates the absence of a software solution to this problem in its most general form. Existing solutions tend to address particular fo...

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems cer- tain algorithms perform well. Most benchmarks are performance based, to test algorithm performance under a wide set of con- ditions. There is also resource- and behavior-based benchmarks to test the resource consumption and the behavior...

Prediction in evolutionary dynamic optimization (EDO), such as predicting the movement of optima, or when and how an environment will change, is a topic that is still under investigation and presents unsolved challenges. A few studies approach prediction based on re-initialising a population or requirement satisfaction problems such as Robust Optim...

The COVID-19 pandemic caused a shift in teaching practice towards blended learning for many higher education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs. This paper is an attempt to summarise and extend pre-COVID-19 pedagogical research to l...

Using an unbalanced panel of 922 non-financial companies publicly listed on the London Stock Exchange during January 1995 and September 2014, this article tests the predictions of Pecking Order Theory (POT), Trade-off Theory (TOT) and Market Timing Theory (MTT) of capital structure through the lens of macroeconomic conditions. We find strong eviden...

The Covid-19 pandemic caused a shift in teaching practice towards blended learning for many Higher Education institutions. This led to the rapid adoption of certain digital technologies within existing teaching structures as a means to meet student access needs and facilitate learning. Integration of these technologies caused numerous challenges fo...

We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple box constraints. Currently, in the field of heuristic optimisation, such specification is rarely mentioned or investig...

Differential Evolution is a popular optimisation method with a small number of parameters. However, different hyper-parameters and Differential Evolution variants such as different mutation operators and the F and Cr parameter may introduce structural bias. Structural bias is a form of bias where artefacts in the algorithm lead to a preference to p...

In this paper, we present a model for a serial robotic system with flexible joints (RFJ) using Euler–Lagrange equations, which integrates the oscillatory dynamics generated by the flexible joints at specific operating points, using a pseudo-Ornstein-Uhlenbeck process with reversion to the mean. We also propose a Stochastic Flexible - Adaptive Neura...

Climate change threats make it difficult to perform reliable and quick predictions on floods forecasting. This gives rise to the need of having advanced methods, e.g., computational intelligence tools, to improve upon the results from flooding events simulations and, in turn, design best practices for riverbed maintenance. In this context, being ab...

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour...

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour...

Training effective simulation scenarios presents numerous challenges from a pedagogical point of view. Through application of the Conceptual Framework for e-Learning and Training (COFELET) as a pattern for designing serious games, we propose the use of the Simulated Critical Infrastructure Protection Scenarios (SCIPS) platform as a prospective tool...

This paper investigates how often the popular configurations of Differential Evolution generate solutions outside the feasible domain. Following previous publications in the field, we argue that what the algorithm does with such solutions and how often this has to happen is important for the overall performance of the algorithm and interpretation o...

Structural Bias (SB) is an important type of algorithmic deficiency within iterative optimisation heuristics. However, methods for detecting structural bias have not yet fully matured, and recent studies have uncovered many interesting questions. One of these is the question of how structural bias can be related to anisotropy. Intuitively, an algor...

Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorithms and therefore require modern heuristics to find feasible solutions quickly. These heuristics and...

The unfolding coronavirus (COVID‐19) pandemic has highlighted the global need for robust predictive and containment tools and strategies. COVID‐19 continues to cause widespread economic and social turmoil, and while the current focus is on both minimising the spread of the disease and deploying a range of vaccines to save lives, attention will soon...

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimisi...

Operational Risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labour relations, clients, damage to fixed assets, technological f...

Compact optimization is an alternative paradigm in the field of metaheuristics requiring a modest use of memory to optimize a problem. As opposed to population-based algorithms, which conduct the search by employing a set of candidate solutions, compact algorithms use a probabilistic model to describe how solutions are distributed over the search s...

Making decisions under uncertainty is very challenging but necessary as most real-world scenarios are plagued by disturbances that can be generated internally, by the hardware itself, or externally, by the environment. Hence, we propose a general decision-making framework which can be adapted to optimally address the most heterogeneous real-world d...

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our appr...

This paper investigates whether optimisation methods with the population made up of one solution can suffer from structural bias just like their multisolution variants. Following recent results highlighting the importance of choice of strategy for handling solutions generated outside the domain, a selection of single solution methods are considered...

This work presents a novel intelligent system designed using a multi-agent hardware platform to detect improvised explosive devices concealed in the ground. Each agent is equipped with a different sensor, (i.e. a ground-penetrating radar, a thermal sensor and three cameras each covering a different spectrum) and processes dedicated AI decision-maki...

This study combines two novel deterministic methods with a Convolutional Neural Network to develop a machine learning method that is aware of directionality of light in images. The first method detects shadows in terrestrial images by using a sliding-window algorithm that extracts specific hue and value features in an image. The second method inter...

The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to...

We present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorit...

Pupil absenteeism remains a significant problem for schools across the globe with its negative impacts on overall pupil performance being well-documented. Whilst all schools continue to emphasize good attendance, some schools still find it difficult to reach the required average attendance, which in the UK is 96\%. A novel approach is proposed to h...

This paper investigates how often the popular configurations of Differential Evolution generate solutions outside the feasible domain. Following previous publications in the field, we argue that what the algorithm does with such solutions and how often this has to happen is important for the overall performance of the algorithm and interpretation o...

In the field of stochastic optimisation, the so-called structural bias constitutes an undesired behaviour of an algorithm that is unable to explore the search space to a uniform extent. In this paper, we investigate whether algorithms from a subclass of estimation of distribution algorithms, the compact algorithms, exhibit structural bias. Our appr...

Real-world problems such as landmine detection require multiple sources of information to reduce the uncertainty of decision-making. A novel approach to solve these problems includes distributed systems, as presented in this work based on hardware and software multi-agent systems. To achieve a high rate of landmine detection, we evaluate the perfor...

The Stochastic Optimisation Software (SOS) is a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. It reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms an...

Developments in artificial intelligence can be leveraged to support the diagnosis of degenerative disorders, such as epilepsy and Parkinson's disease. This study aims to provide a software solution, focused initially towards Parkinson's disease, which can positively impact medical practice surrounding de-generative diagnoses. Through the use of a d...

Robust Optimization Over Time (ROOT) is a new method of solving Dynamic Optimization Problems in respect to choosing a robust solution, that would last over a number of environment changes, rather than the approach that chooses the optimal solution at every change. ROOT methods currently show that ROOT can be solved by predicting an individual fitn...

This article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available onlin...

Motivated by the limited work performed on the development of computational techniques for solving the nonlinear Schrödinger equation with time-dependent coefficients, we develop a modified Runge-Kutta pair with improved periodicity and stability characteristics. Additionally, we develop a modified step size control algorithm, which increases the e...

This paper investigates whether optimisation methods with the population made up of one solution can suffer from structural bias just like their multisolution variants. Following recent results highlighting the importance of choice of strategy for handling solutions generated outside the domain, a selection of single solution methods are considered...

Computational Intelligence (CI), originally represented by the three subjects of Evolutionary Computation (EC), Fuzzy Logic (FL) and Neural Networks (NNs), has significantly evolved to date and reached high levels of hybridisation and complexity. Starting from fuzzy neural or genetic systems, to the more recent computational frameworks such as deep...

This article presents the Optimised Stream clustering algorithm (OpStream), a novel approach to cluster dynamic data streams. The proposed system displays desirable features, such as a low number of parameters and good scalability capabilities to both high-dimensional data and numbers of clusters in the dataset, and it is based on a hybrid structur...