Felipe Campelo

Felipe Campelo
Aston University · Department of Computer Sciences

Ph.D.

About

90
Publications
13,445
Reads
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1,084
Citations
Citations since 2016
39 Research Items
586 Citations
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
2016201720182019202020212022020406080100
Introduction
Felipe Campelo is a Lecturer in Computer Science at Aston University, where he is in charge of teaching Data Mining and Data Science Programming. His current research focuses on the development of integrated solution frameworks for data analytics, seamlessly connecting data mining, statistical modelling, optimisation and multi-criteria decision making. His research also focusses on the development of methodologically and statistically sound protocols for the experimental comparison of algorithms, and with the development of algorithms for several aspects of optimisation. His interests also include the development and application of Data Mining methodologies for a wide range of application areas, from Smart Grids to Bioinformatics.
Additional affiliations
August 2010 - present
Federal University of Minas Gerais
Position
  • Professor (Associate)
January 2009 - December 2010
Federal University of Minas Gerais
January 2004 - December 2009
Hokkaido University
Education
October 2014 - December 2015
Johns Hopkins Bloomberg School of Public Health / Coursera
Field of study
  • Data Science
April 2006 - January 2009
Hokkaido University
Field of study
  • Systems Science and Tecnology
October 2004 - February 2006
Hokkaido University
Field of study
  • Systems Science and Tecnology

Publications

Publications (90)
Conference Paper
Full-text available
Over recent decades, power distribution systems have been required to handle extreme events that are increasing in both frequency and intensity. In addition, there are novel technologies, planning strategies, and operational approaches. In this context, topological flexibility is a key aspect to allow distribution systems to accommodate all the sim...
Preprint
Full-text available
Monkeypox is a disease caused by the Monkeypox virus (MPXV), a double-stranded DNA virus from genus Orthopoxvirus under family Poxviridae, that has recently emerged as a global health threat after decades of local outbreaks in Central and Western Africa. Effective epidemiological control against this disease requires the development of cheaper, fas...
Article
Tissue testing used to assess the chemical contents in potato plants is considered laborious, time-consuming, destructive, and expensive. Ground-based sensors have been assessed to provide efficient information on nitrogen using leaf canopy reflectance. In potatoes, however, the main organ required for tissue testing is the petiole to estimate the...
Article
During epidemics, data from different sources can provide information on varying aspects of the epidemic process. Serology-based epidemiologic surveys could be used to compose a consistent epidemic scenario. We assessed the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG in serum samples collected from 7,837 blood...
Chapter
Data availability in a wide variety of domains has boosted the use of Machine Learning techniques for knowledge discovery and classification. The performance of a technique in a given classification task is significantly impacted by specific characteristics of the dataset, which makes the problem of choosing the most adequate approach a challenging...
Preprint
Full-text available
Background The identification of linear B-cell epitopes remains an important task in the development of vaccines, therapeutic antibodies and several diagnostic tests. Machine learning predictors are trained to flag potential epitope candidates for experimental validation and currently, most predictors are trained as generalist models using large, h...
Preprint
Full-text available
Background The development of peptide-based diagnostic tests requires the identification of epitopes that are at the same time highly immunogenic and, ideally, unique to the pathogen of interest, to minimise the chances of cross-reactivity. Existing computational pipelines for the prediction of linear B-cell epitopes tend to focus exclusively on th...
Preprint
Full-text available
The increasing availability of genomic, annotation, evolutionary and phenotypic data for species contrasts with the lack of studies that adequately integrate these heterogeneous data sources to produce biologically meaningful knowledge. Here, we present CALANGO, a phylogeny-aware comparative genomics tool that uncovers functional molecular converge...
Article
Full-text available
Motivation: In silico identification of linear B-cell epitopes represents an important step in the development of diagnostic tests and vaccine candidates, by providing potential high-probability targets for experimental investigation. Current predictive tools were developed under a generalist approach, training models with heterogeneous data sets...
Chapter
A new selection operator for genetic algorithms dedicated to combinatorial optimization, the Diversity Driven selection operator, is proposed. The proposed operator treats the population diversity as a second objective, in a multiobjectivization framework. The Diversity Driven operator is parameterless, and features low computational complexity. Nu...
Article
Full-text available
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as par...
Preprint
Full-text available
We demonstrate improved performance in the classification of bioelectric data for use in systems such as robotic prosthesis control, by data fusion using low-cost electromyography (EMG) and electroencephalography (EEG) devices. Prosthetic limbs are typically controlled through EMG, and whilst there is a wealth of research into the use of EEG as par...
Article
Full-text available
This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical pow...
Preprint
Full-text available
We propose the cone epsilon-dominance approach to improve convergence and diversity in multiobjective evolutionary algorithms (MOEAs). A cone-eps-MOEA is presented and compared with MOEAs based on the standard Pareto relation (NSGA-II, NSGA-II*, SPEA2, and a clustered NSGA-II) and on the epsilon-dominance (eps-MOEA). The comparison is performed bot...
Article
Full-text available
Multiobjective evolutionary algorithms based on decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easie...
Preprint
Full-text available
Recent studies on resource allocation suggest that some subproblems are more important than others in the context of the MOEA/D, and that focusing on the most relevant ones can consistently improve the performance of that algorithm. These studies share the common characteristic of updating only a fraction of the population at any given iteration of...
Chapter
Full-text available
This work presents an image classification approach to EEG brainwave classification. The proposed method is based on the representation of temporal and statistical features as a 2D image, which is then classified using a deep Convolutional Neural Network. A three-class mental state problem is investigated, in which subjects experience either relaxa...
Article
Tuning parameters is an important step for the application of metaheuristics to specific problem classes. In this work we present a tuning framework based on the sequential optimisation of perturbed regression models. Besides providing algorithm configurations with good expected performance, the proposed methodology can also provide insights on the...
Conference Paper
Full-text available
This work presents an image classification approach to EEG brainwave classification. The proposed method is based on the representation of temporal and statistical features as a 2D image, which is then classified using a deep Convolutional Neural Network. A three-class mental state problem is investigated, in which subjects experience either relaxa...
Preprint
Full-text available
This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical pow...
Article
Full-text available
Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class. The proposed approach allows the experime...
Article
After the occurrence of faults in a radial distribution system, the load restoration problem consists in implementing a sequence of switch opening and closing operations such that the resulting network configuration restores services to the most loads in the shortest possible time. We formulate this optimization problem in terms of two complementar...
Article
When a fault occurs in a power distribution network, energy utilities have limited time to define and run a restoration plan. While this problem is widely studied in the literature, existing works consider neither the work in parallel of multiple maintenance teams, nor the time taken by the teams to move between locations. In this paper, we address...
Preprint
Full-text available
Tuning parameters is an important step for the application of metaheuristics to problem classes of interest. In this work we present a tuning framework based on the sequential optimization of perturbed regression models. Besides providing algorithm configurations with good expected performance, the proposed methodology can also provide insights on...
Article
Purpose This paper aims to present a SPICE model to represent antennas in receiving mode. The model can be used to evaluate the performance of the antenna when it is coupled to several different nonlinear electric circuits. The proposed methodology is particularly suitable for rectenna applications, as it allows the analysis of different configurat...
Preprint
Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class. The proposed approach allows the experime...
Preprint
Full-text available
Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) represent a widely used class of population-based metaheuristics for the solution of multicriteria optimization problems. We introduce the MOEADr package, which offers many of these variants as instantiations of a component-oriented framework. This approach contributes for easie...
Article
This work introduces a heuristic for mixed integer programming (MIP) problems with binary variables, based on information obtained from differences between feasible solutions as well as solutions from the linear relaxation. This information is used to build a neighborhood that is explored as a sub‐MIP problem. The proposed heuristic is evaluated us...
Conference Paper
This paper addresses the problem of planning and allocation of trucks in open-pit mines in terms of three conflicting objectives, and adapts three algorithms for its solution: NSGA-II, SPEA2, and a variant of the Pareto Iterated Local Search using Reduced Variable Neighborhood Search as its local exploration mechanism. Results on four different min...
Conference Paper
We propose a technique for incorporating robustness as part of the search process of evolutionary multiobjective optimization algorithms. The proposed approach calculates the sensitivity of candidate solutions by solving a linear programming subproblem, defined by regression models fitted using points in the neighborhood of each candidate solution....
Conference Paper
This paper discusses the use of self-organizing maps (SOM) for decision-making in multiobjec- tive design problems. Estimates of the Pareto-optimal solutions for a given problem are mapped onto a two- dimensional grid, where the distance between solutions is a measure of their similarity in the parameter space. By comparing the clustered points and...
Conference Paper
This paper presents a systematic investigation of the effects of sixteen recombination operators for real-coded spaces on the performance of Differential Evolution. A unified description of the operators in terms of mathematical operations of vectors is presented, and a standardized implementation is provided in the form of an R package. The object...
Conference Paper
Evolutionary multiobjective optimization is employed for designing the geometric configurations of winglets adapted to a base wing. Seven decision variables are employed for the winglet parameterization, and the wing-winglet transition region is modeled using Bezier surfaces. A case study is presented to illustrate the application of this technique...
Article
Strategic asset management of transformer fleets in electrical power systems is a critical aspect for distribution utilities. In this study, an integrated framework for multicriteria asset management is presented. A multiobjective optimisation model is deployed, simultaneously minimising the maintenance costs and the predicted cost of unexpected fa...
Conference Paper
Full-text available
This work presents theoretical results on the development of a statistical convergence criterion for evolutionary algorithms. An analytical formula is derived for the probability of success in isotropic Gaussian mutation operators over spherical functions, and statistical criteria are proposed for evaluating, with predefined confidence levels, the...
Article
Full-text available
This paper presents a technique that incorporates preference information within the framework of multi-objective evolutionary algorithms for the solution of many-objective optimization problems. The proposed approach employs a single reference point to express the preferences of a decision maker, and adaptively biases the search procedure toward th...
Conference Paper
Full-text available
This work presents a comparison of results obtained by different methods for the Multiobjective Open-Pit Mining Operational Planning Problem, which consists of dynamically and efficiently allocating a fleet of trucks with the goal of maximizing the production while reducing the number of trucks in operation, subject to a set of constraints defined...
Article
This paper presents a fast shape optimization method for antennas using proper orthogonal decomposition (POD)-based model order reduction (MOR). In this method, the finite element region is subdivided into design and ambient regions. The former includes antennas, whose shapes are optimized, whereas the latter includes air and perfect matched layer....
Article
This paper presents parameter and topology optimizations of wideband antennas for microwave energy harvesters based on finite difference time-domain computations. The antenna shapes are optimized to reduce return losses in a specific frequency band using micro-genetic algorithm. The shape parameters of a two-arm planar spiral antenna are optimized....
Conference Paper
Full-text available
This work presents a differential evolution algorithm for combinatorial optimization, in which a set-based representation and operators define subproblems that are used to explore the search space. The proposed method is tested on the capacitated centered clustering problem.
Conference Paper
The Island Model (IM) is a well known multi-population ap-proach for Evolutionary Algorithms (EAs). One of the crit-ical parameters for defining a suitable IM is the migration topology. Basically it determines the Migratory Flows (MF) between the islands of the model which are able to improve the rate and pace of convergence observed in the EAs cou...
Article
Purpose – The purpose of this paper is to apply an Ant colony optimization approach for the solution of the topological design of interior permanent magnet (IPM) machines. Design/methodology/approach – The IPM motor design domain is discretized into a suitable equivalent graph representation and an Ant System (AS) algorithm is employed to achieve...
Article
We present an adaption on the formulation for the vehicle routing problem with fixed delivery and optional collections, in which the simultaneous minimization of route costs and of collection demands not fulfilled is considered. We also propose a multiobjective version of the iterated local search (MOILS). The performance of the MOILS is compared w...
Conference Paper
Differential evolution (DE) was originally designed to solve continuous optimization problems, but recent works have been investigating this algorithm for tackling combinatorial optimization (CO), particularly in permutation-based combinatorial problems. However, most DE approaches for combinatorial optimization are not general approaches to CO, be...
Conference Paper
Finding Nash equilibria has been one of the early objectives of research in game theory, and still represents a challenge to this day. We introduce a multiobjective formulation for computing Pareto-optimal sets of mixed Nash equilibria in normal form games. Computing these sets can be notably useful in decision making, because it focuses the analys...
Conference Paper
Full-text available
We examine the performance of four discrete differential evolution (DE) algorithms for the solution of capacitated vehicle routing problems (CVRPs). Twenty seven test instances are employed in the experimental analysis, with comparisons of final solution quality and time to convergence. The results indicate that two approaches presented significant...
Conference Paper
Full-text available
This work explores the influence of three different dominance criteria, namely the Pareto-, epsilon-, and cone epsilon-dominance, on the performance of multiobjective evolutionary algorithms. The approaches are incorporated into two different algorithms, which are then applied to the solution of twelve benchmark problems from the ZDT and DTLZ famil...
Conference Paper
Full-text available
In this paper, a new approach to the topology configuration problem in the Island Model (IM) is proposed. The mechanism proposed works with a pool of candidates for migration and the choice of immigrants is performed using the usual selection techniques of evolutionary algorithms. Computational tests on IM versions of the Differential Evolution sho...
Chapter
Full-text available
Este capítulo apresenta uma abordagem multiobjetivo para o problema de roteamento de veículos com coleta seletiva, cujos objetivos são a minimização dos custos das rotas e das demandas de coletas não atendidas. Propõe-se uma estrutura de dados que melhor se ajusta ao problema, estruturas de vizinhanças que exploram ambos objetivos do problema e um...
Article
This paper presents the optimization of a meander line antenna finite-element model by means of an adaptive genetic algorithm (GA). To search for optimal antenna configurations, the present method employs a GA with an adaptive method that adjusts the characteristics of its selection, crossover, and mutation operators in order to maintain a diverse...
Article
In a Two-Level Reverse Distribution Network, products are returned from customers to manufacturers through collection and refurbishing sites. The costs of the reverse chain often overtake the costs of the forward chain by many times. With some known algorithms for the problem as reference, we propose a hybrid memetic algorithm that uses linear prog...
Chapter
Full-text available
Reverse Distribution Networks are designed to plan the distribution of products from customers to manufacturers. In this paper, we study the problem with two-levels,with products transported from origination points to collection sites before being sent to a refurbishing site. The optimization of reverse distribution networks can reduce the costs of...
Conference Paper
The Island Model (IM) is an important multi-population approach for improving the performance of Evolutionary Algorithms (EAs) when solving complex problems. One of the critical parameters for defining a suitable IM is the migration topology, which determines the migratory flows between sub-populations of the model. Despite the importance of this p...
Article
Purpose The purpose of this paper is to present a graph representation of the design space that is suitable for the ant colony optimization (ACO) method in topology optimization (TO) problems. Design/methodology/approach The ACO is employed to obtain optimal routes in an equivalent graph representation of the discretized design space, with each ro...
Conference Paper
Full-text available
In this paper, we analyze four dominance criteria in terms of their ability to adequately order sets of points in multi- and many-objective optimization problems. The use of relaxed and alternative dominance relationships has been an important tool for improving the performance of multiobjective evolutionary optimization algorithms, and their order...
Conference Paper
Full-text available
Relaxed forms of Pareto dominance have been shown to be the most effective way in which evolutionary algorithms can progress towards the Pareto-optimal front with a widely spread distribution of solutions. A popular concept is the ε-dominance technique, which has been employed as an archive update strategy in some multiobjective evolutionary algori...
Article
Full-text available
This paper presents parameter and topology optimization of inductor shapes using evolutionary algorithms. The goal of the optimization is to reduce the size of inductors satisfying the specifications on inductance values under weak and strong bias-current conditions. The inductance values are computed from the finite-element (FE) method taking magn...
Conference Paper
Full-text available
We propose in this paper a new strategy for self-adaptation in multiobjective evolutionary algorithms, which is based on information obtained from the implicit distribution created by a chaotic differential mutation operator. This technique is used to develop a self-adaptive evolutionary algorithm for multiobjective optimisation, and its efficiency...
Article
In this paper, we introduce an approach for the design of electromagnetic devices based on the use of estimation of distribution algorithms (EDAs), coupled with approximation-based local search around the most promising solutions. The main idea is to combine the power of EDAs in the solution of hard optimization problems with the faster convergence...
Article
Topology optimization (TO) is a promising field for the design of industrial devices, since it can allow computational tools to develop creative solutions independently of initial topologies. However, the generation of smooth solutions, i.e., solutions that can be physically implemented, remains a challenge for most TO techniques available. In this...