Myriam R. Delgado

• Federal University of Technology of Paraná

Location-Based Social Networks (LBSNs) are valuable for understanding urban behavior and providing useful data on user preferences. Modeling their data into graphs like interest networks (iNETs) offers important insights for urban area recommendations, mobility forecasting, and public policy development. This study uses check-ins and venue reviews...

Urban transportation planning in densely populated areas is a problem in constant need of efficient solutions. Graphs can represent urban street networks and be used to train algorithms, enriching decisions with information learned from structural and topological data of cities. Relational Fusion Networks (RFNs) are Graph Neural Networks specifical...

Visual sentiment analysis is a challenging problem. Many datasets and approaches have been designed to foster breakthroughs in this trending research topic. However, most works scrutinize only subsymbolic models through visual attributes of the evaluated images, paying less attention to the subjectivity of viewers' perceptions as a basis for neuro-...

Per-instance algorithm configuration (PIAC) is an important task in which, given a base problem instance, a recommendation model indicates the best configuration to solve it. Whereas typical Automated Algorithm Configuration (AAC) prescribes configurations for a fixed set of instances, in PIAC, a model is trained using problem features and past exp...

Hyper-heuristics (HH) emerged as more generalized and robust solutions for combinatorial optimization, being successfully addressed to solve several real-world problems. Implemented within an association of MOEA/DD and Differential Evolution, four selection hyper-heuristics (high-level heuristics) are studied in this work: Thompson Sampling, Probab...

A better understanding of the behavior of tourists is strategic for improving services in the competitive and important economic segment of global tourism. Critical studies in the literature often explore the issue using traditional data, such as questionnaires or interviews. Traditional approaches provide precious information; however, they impose...

This paper proposes PredicTour, an approach to process check-ins made by users of location-based social networks (LBSNs), and predict mobility patterns of tourists visiting new countries with or without previous visiting records. PredicTour is composed of three key parts: mobility modeling, profile extraction, and tourist mobility prediction. In th...

Hyper-heuristics (HHs) are algorithms suitable for designing heuristics. HHs perform the search divided in two levels: they look for heuristic components in the high level and the heuristic is used, in the low level, to solve a set of instances of one or more problems. Different from offline HHs, hyper-heuristics with dynamic learning select or gen...

As most of Multi-Objective Evolutionary Algorithms (MOEAs) scale quite poorly when the number of objective functions increases, new strategies have been proposed to face this limitation. Considered one of the most well-succeeded examples of such new strategies, the third version of Non-dominated Sorting Genetic Algorithm (NSGA-III) uses a set of re...

This work proposes a multilevel framework called Treasure Hunt, which is capable of distributing independent search algorithms to a large number of processing nodes. Treasure Hunt ensures quick propagation of information and explores a driving mechanism, obtaining joint convergences between working nodes. Experiments on classic, random and competit...

Flowshop problems (FSPs) have many variants and a broad set of heuristics proposed to solve them. Choosing the best heuristic and its parameters for a given FSP instance can be very challenging for practitioners. Per-instance Algorithm Configuration (PIAC) approaches aim at recommending the best algorithm configuration for a particular instance pro...

The Algorithm Selection Problem (ASP) considers the use of previous knowledge regarding problem features and algorithm performance to recommend the best strategy to solve a previously unseen problem. In the application context, the usual ASP for optimization considers recommending the best heuristics, whenever it faces a new similar problem instanc...

As redes sociais baseadas em localização (LBSNs) disponibilizam uma nova variedade de possibilidades para obtenção de dados em larga escala, especialmente com o significativo aumento de usuários de mídias sociais. Nesse sentido, a proposta do trabalho atual é explorar dados de LBSNs para estudar as influências regionais no comportamento de turistas...

In this work, we explore different multi-armed bandit-based hyper-heuristics applied to the multi-objective permutation flow shop problem. It is a scheduling problem which has been extensively studied due to its relevance for industrial engineering. Three multi-armed bandit basic formulations are used in the hyper-heuristic selection mechanism: (i)...

Este artigo aborda o Flow Shop de Permutação, um problema de sequenciamento presente em muitos mecanismos de gerenciamento de processos de produção industrial. A abordagem multiobjetivo considerada neste trabalho envolve a minimização do tempo máximo para completar um trabalho (makespan) e do tempo total de atraso (total tardiness). Para isso é uti...

In recent years, research focused on (semi)automatic radiographic inspection methods has gained more attention. The present work proposes a method for detecting defects in radiographic images of welded joints of oil pipes. Real condition images obtained by the double wall double image (DWDI) technique usually present a lower quality when compared w...

In the context of recommendation methods, meta-learning considers the use of previous knowledge regarding problems solution and performance to indicate the best strategy, whenever it faces a new similar problem. This paper studies the use of meta-learning to recommend local search strategies to solve several instances of permutation flowshop proble...

This paper describes a method to support the field of Nondestructive Testing, especially, in radiographic inspection activities. It aims at detecting welded joints of oil pipelines in radiographs with Double Wall Double Image exposure. The proposed approach extracts information (windows of pixels) from the pipeline region in the radiographic image...

This paper proposes a new clustering method called Gustafson-Kessel with Focal Point (GKFP). The proposal aims at benefiting from the advantage of using Gustafson-Kessel clustering technique leveraged by the use of a Focal Point which enables obtaining partitions with different levels of granularity. Thus the method identifies clusters with uncorre...

The Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA) has shown to be very competitive for Many Objective Optimization Problems (MaOPs). The Probabilistic Graphic Model (PGM) of HMOBEDA expands the possibilities for exploration as it provides the joint probability of decision variables, objectives, and configuration par...

Local Optima Networks are models proposed to understand the structure and properties of combinatorial landscapes. The fitness landscape is explored as a graph whose nodes represent the local optima (or basins of attraction) and edges represent the connectivity between them. In this paper, we use this representation to study a combinatorial optimisa...

Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics. In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (mBOA) on instances of MNK-lands...

In this paper, we investigate the use of hyper-heuristics for the travelling thief problem (TTP). TTP is a multi-component problem, which means it has a composite structure. The problem is a combination between the travelling salesman problem and the knapsack problem. Many heuristics were proposed to deal with the two components of the problem sepa...

Nowadays, a number of metaheuristics have been developed for efficiently solving multi-objective optimization problems. Estimation of distribution algorithms are a special class of metaheuristic that intensively apply probabilistic modeling and, as well as local search methods, are widely used to make the search more efficient. In this paper, we ap...

The radiographic inspection of weld beads is important to ensure quality and safety in pipe networks. Visual fatigue, distractions, and the amount of radiographic images to be analyzed can be listed as main factors for human inspection errors. This chapter presents an approach for automatically segmenting weld beads in Double Wall Double Image (DWD...

Hyper-heuristics are high-level search techniques which improve the performance of heuristics operating at a higher heuristic level. Usually, these techniques automatically generate or select new simpler components based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, u...

When dealing with metaheuristics, one important question is how many evaluations are worth spending in the search for better results. This work proposes a method to estimate the best moment to stop swarm iterations based on the analysis of the convergence behavior presented during optimization, aiming to provide an effective balance between saving...

A bearing is an essential component in rotating machinery, one of its principal cause of failure, and its health condition is directly related to the safety and effective operation of such machinery. To the best of our knowledge, it is the first time that a probabilistic fuzzy system is applied to bearing fault classification. The type of probabili...

The paper presents an automatic rule-base design of probabilistic fuzzy systems developed for classification tasks. The objective here is to present a methodology that allows the user to obtain a fuzzy classifier directly from training data, in which rules' antecedents are defined on the basis of clustering techniques and probabilistic consequents...

The number of objectives in real-world problems has increased in recent years and better algorithms are needed to deal efficiently with it. One possible improvement to such algorithms is the use of adaptive operator selection mechanisms in many-objective optimization algorithms. In this work, two adaptive operator selection mechanisms, Probability...

Probabilistic modeling of selected solutions and incorporation of local search methods are approaches that can notably improve the results of multi-objective evolutionary algorithms (MOEAs). In the past, these approaches have been jointly applied to multi-objective problems (MOPs) with excellent results. In this paper, we introduce for the first ti...

Este artigo apresenta uma metodologia para a detecção do tubo em imagens radiográficas do tipo parede dupla vista dupla (PDVD) de tubulações condutoras de petróleo. O principal objetivo da proposta é reduzir a região de busca através da delimitação da área do tubo para a extração automática do cordão de solda auxiliando, desta forma, a posterior de...

Particle Swarm Optimization (PSO) is a metaheuristic inspired on the emerging social behavior found in nature. PSO has shown good results in some recent works of discrete optimization, even though it was originally designed for continuous optimization problems. This paper models the problem of allocating a set of cabs to some customers as a combina...

Protein Structure Prediction (PSP) is the process of determining three-dimensional structures of proteins based on their sequence of amino acids. PSP is of great importance to medicine and biotechnology, e.g., to novel enzymes and drugs design, and one of the most challenging problems in bioinformatics and theoretical chemistry. This paper models P...

Understanding nodes mobility is of fundamental importance for data delivery in opportunistic and intermittently connected networks referred to as Delay Tolerant Networks (DTNs). The analysis of such mobility patterns and the understanding of how mobile nodes interact play a critical role when designing new routing protocols for DTNs. The Cultural G...

The city of Curitiba, located at Southern Brazil, is recognized by its urban planning structured on three pillars: land use, collective transportation, and traffic. With 3.8 million people in its metropolitan area, the public transport system deals with approximately 2.5 million passengers daily. The structure and properties of such a transportatio...

This paper proposes ELMOEA/D, a surrogate-assisted MOEA, for solving costly multi-objective problems in small evaluation budgets. The proposed approach encompasses a state-of-the-art MOEA based on decomposition and Differential Evolution (MOEA/D-DE) assisted by Extreme Learning Machines (ELMs). ELMOEA/D is tested in instances from three well-known...

Particle Swarm Optimization (PSO) is a relatively recent meta-heuristic inspired by the swarming or collaborative behaviour of biological populations. It is known by its capacity of obtaining important fitness improvements on a short period of time. A cooperative version named CPSO has been used to deal with high dimensional search spaces and CCPSO...

Hyper-Heuristics are high-level methodologies developed to select or generate heuristics for solving complex problems. Despite their success, there is a lack of multi-objective hyper-heuristics. In the multi-objective optimization context, MOEA/D decomposes a problem into a number of sub problems handled by individuals in a collaborative manner. Ou...

This paper proposes a modification in the Fuzzy Particle Swarm Clustering (FPSC) algorithm such that membership degrees are used to weight the step size in the direction of the local and global best particles, and in its movement in the direction of the input data at every iteration. This results in the so-called Membership Weighted Fuzzy Particle...

Designed to Delay Tolerant Networks, the Cultural Greedy GrAnt (CGrAnt) routing protocol uses Ant Colony Optimization to represent the population space of a Cultural Algorithm. CGrAnt aims to improve the message forwarding by analyzing the network characteristics based on three distinct knowledge: Domain, History, and Situational. The Domain knowle...

The multi-objective Quadratic Assignment Problem (mQAP) is a hard optimization problem with many real-world applications, such as in hospital layouts. The main purposes of this paper are: (1) the investigation of hybrid algorithms combining Transgenetic Algorithms and Evolutionary Multi-objective Optimization (EMO) frameworks to deal with mQAP and...

Despite the success of Evolutionary Algorithms in solving complex problems, they may require many function evaluations. This becomes an issue when dealing with costly problems. Surrogate models may overcome this difficulty, though their use in problems with medium to large dimensionality is underexplored in the literature. Problems with multiple co...

This paper addresses the automatic definition of rule-bases in Probabilistic Fuzzy Systems (PFS). The proposed approach adopts a technique called agglomerative hierarchical clustering to perform the partition of the input variables’ universes. Statistical measures as mean and variance, calculated from the clusters identified in n-dimensional spaces...

This paper presents an approach for continuous optimization called Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D). The approach incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of strategies adaptation. In this work we test two methods to perform adaptive strategy...

A detecção de descontinuidades em radiografias de soldas não é um trabalho trivial para a automatização via software. Há fatores que prejudicam a determinação de uma metodologia única em todas as imagens digitais, primeiramente, devido à complexidade em discernir defeitos que, em grande parte, têm características semelhantes a outras indicações...

Protein Structure Prediction (PSP) is one of the most challenging problems in Bioinformatics research area. This paper models PSP as a multiobjective optimization problem and adopts Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D) to minimize potential energies (bonded and non-bonded) providing final protein structures. ADEMO/D...

This paper proposes a PSO-based optimization approach with a particular path relinking technique for moving particles. PSO is evaluated for two combinatorial problems. One under uncertainty, which represents a new application of PSO with path relinking in a stochastic scenario. PSO is considered first in a deterministic scenario for solving the Tas...

This work considers Artificial Immune Systems to solve the economic load dispatch problem. The Immune Systems are based on the clonal selection principle. Cultural Algorithms using normative, situational, historical and topographical knowledge sources are used to improve the global optimization property of immune systems. A new main influence funct...

A supply chain (SC) can be characterized by a path followed by a product since its manufacturing until its final distribution at a customer market including raw material providers. This paper characterizes the behavior of a SC composed by three production levels (retail, distribution and manufacturing) using a simulation model which can be used for...

This article describes an experiment using an adaptive neuro-fuzzy inference system (ANFIS) to classify epileptiform events in electroencephalographic (EEG) signals. The experiment uses Wavelet Transform (WT) to extract features from the signal. The extracted features are statistically calculated from the resulting wavelet coefficients and these da...

This article describes an experiment using fuzzy logic for recognition of crackles in respiratory sounds. It presents the theory and the separation of crackles from vesicular sounds (VS). Some articles which use similar techniques to the experiment are related and compared. The experiment uses wavelet transform (WT) to separate crackles which are u...

This paper proposes a method for continuous optimization based on Differential Evolution (DE). The approach named Adaptive Differential Evolution for Multiobjective Problems (ADEMO/D) incorporates concepts of Multiobjective Evolutionary Algorithms based on Decomposition (MOEA/D) and mechanisms of mutation strategies adaptation inspired by the adapt...

Particle Swarm Optimization (PSO) is based on the analysis of emergent behavior of bird flocks. Though it was originally designed for continuous optimization, PSO has provided good results in some recent works when applied to static and discrete optimization problems. In this paper, the particle encoding scheme is based on permutations and the PSO...

Given a set of markets and a set of products to be purchased on those markets, the Biobjective Traveling Purchaser Problem (2TPP) consists in determining a route through a subset of markets to collect all products, minimizing the travel distance and the purchasing cost simultaneously. As its single objective version, the 2TPP is an NP-hard Combinat...

This paper proposes a fuzzy version of the crisp cPSC (Constructive Particle Swarm Clustering), called FcPSC (Fuzzy Constructive Particle Swarm Clustering). In addition to detecting fuzzy clusters, the proposed algorithm dynamically determines a suitable number of clusters in the datasets without the need of prior knowledge, necessary in cPSC to co...

This paper proposes an approach for rule base design that considers most of information obtained from a data set. The proposed method provides fuzzy rules where each consequent label has an associated weight which is determined in a probabilistic way. The paper also presents the method’s formalization using crisp and fuzzy relations and their assoc...

This paper presents a new routing protocol for Delay Tolerant Networks (DTNs), based on a distributed swarm intelligence approach. The protocol is called Cultural Greedy Ant (CGrAnt), as it uses a Cultural Algorithm (CA) and a greedy version of the Ant Colony Optimization (ACO) metaheuristic. The term greedy implies the use of a deterministic trans...

This paper presents a pioneering approach for weld bead detection in radiographic images obtained by the Double Wall Double Image (DWDI) technique. Such task constitutes an essential step for several high level processes, such as fully automatic flaw identification on welded joints. Sets of sample pixels, corresponding to candidate solutions provid...

Data clustering is useful in several areas, such as web mining, biology, climate, medical diagnosis, computer vision, marketing and others. Thus, in real problems, data can simultaneously belong to more than one cluster, being necessary to use fuzzy clustering concepts as decision mechanisms to assign data into clusters. Moreover, nature-based inte...

This paper presents a new prediction-based forwarding protocol for complex and dynamic delay tolerant networks (DTNs). The proposed protocol is called GrAnt (Greedy Ant), as it uses the Ant Colony Optimization (ACO) metaheuristic with a greedy transition rule. This allows GrAnt to select the most promising forwarder nodes or allow for the exploitat...

Protein structure prediction (PSP) is one of the most challenging problems nowadays and an important Bioinformatics research topic. In this paper we propose an optimization method based on differential evolution for PSP problem. We model PSP as an optimization problem in order to minimize the potential energy using ab initio approach. This problem...

This paper proposes a new prediction-based routing protocol for Delay Tolerant Networks (DTNs) called Greedy Ant (GrAnt). GrAnt uses a greedy transition rule of the Ant Colony Optimization (ACO) metaheuristic to provide the exploitation of good previous solutions, when available, and to forward the messages to the most promising node(s). By making...

This paper proposes a simple and innovative method to design rule bases for inference systems by joining well known theories to treat uncertainty, such as probability and fuzzy systems. The rule base design is based on some modifications in the Wang-Mendel method in the sense that all the information obtained from the training set can be considered...

This paper proposes the Fuzzy Particle Swarm Clustering (FPSC) algorithm, which is an extension of the crisp data clustering algorithm PSC particularly tailored to deal with fuzzy clusters. The main structural changes of the original PSC algorithm to design FPSC occurred in the selection and evaluation steps of the winner particle, comparing the de...

This work proposes an algorithm based on Computational Transgenetic (CT) metaphor to deal with the bi-objective traveling purchaser problem (2TPP). The 2TPP consists in determining a route through a subset of markets to collect a set of products, minimizing the travel distance and the purchasing cost simultaneously. This problem contains a finite s...

This paper presents intelligent systems based on natural computing for solving economic load dispatch problems. The proposed methods use three different natural computing techniques: Cultural algorithms, artificial immune systems and fuzzy inference systems. The base for the methods is a real coded immune system centred on the clonal selection prin...

Nowadays a great effort has been spent by companies to improve their logistics in terms of programming of events that affect production and distribution of products. In this case, simulation can be a valuable tool for evaluating different behaviors. The objective of this work is to build a discrete event simulation model for scheduling of operation...

A utilização de recursos e infra-estrutura de maneira planejada pode, em operações de produção e transporte, trazer bons resultados econômicos, pois diminui a ociosidade e aumenta a eficiência do sistema. Toda empresa que realiza este tipo de operação busca escalonamentos de tarefas eficientes e, através da simulação, é possível reproduzir o compor...

This paper addresses a soft computing-based approach to design soft sensors for industrial applications. The goal is to identify second-order Takagi–Sugeno–Kang fuzzy models from available input/output data by means of a coevolutionary genetic algorithm and a neuro-based technique. The proposed approach does not require any prior knowledge on the...

This paper aims to incorporate intelligent mechanisms based on Soft Computing in Geographical Information Systems (GIS). The proposal here is to present a spatio-temporal prediction method of forestry evolution for a sequence of binary images by means of fuzzy inference systems (FIS), genetic algorithm (GA) and genetic programming (GP). The main in...

Artificial Immune Systems (AIS) represent one of the most recent and promising approaches in the branch of bio-inspired techniques. Although this open field of research is still in its infancy, several relevant results have been achieved by using the AIS paradigm in demanding tasks such as the ones coming from computational biology and biochemistry...

This paper presents two versions of a general type-2 fuzzy classifier. The focus is on interpretability since the rules are meaningful and the rule base is comprised of few rules, which is a direct consequence of the hierarchical reclassification process being proposed. The approaches are evaluated on a land cover classification problem by using da...

Non-linear multiple-input multiple-output (MIMO) processes which are common in industrial plants are characterized by significant interactions and non- linearities among their variables. Thus, tuning several controllers in complex industrial plants is a challenge for process engineers and operators. An approach for adjusting the parameters of n pro...

Genomic DNA copy number aberrations are frequent in solid tumours although their underlying causes of chromosomal instability in tumours remain obscure. In this paper we show how Artificial Immune System (AIS) paradigm can be successfully employed in the elucidation of biological dynamics of cancerous processes using a novel fuzzy rule induction s...

This paper proposes a fuzzy classifier based on type-2 fuzzy sets to be applied in land cover classification. The classifier is built on the basis of the available data and considers the merging of information drawn from different experts. The data regard a thematic mapper representing the land cover of a real plain cultivated area. The experts are...

This work proposes two versions of an Artificial Immune System (AIS) - a relatively recent computational intelligence paradigm – for predicting protein functions described in the Gene Ontology (GO). The GO has functional classes (GO terms) specified in the form of a directed acyclic graph, which leads to a very challenging multi-label hierarchical...