Antonio Della Cioppa

• Physics
• Professor (Associate) at University of Salerno

In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the...

Diabetes mellitus is a metabolic disease involving high blood glucose levels that can lead to serious medical consequences. Hence, for diabetic patients the prediction of future glucose levels is essential in the management of the disease. Most of the forecasting approaches in the literature evaluate the effectiveness of glucose predictors only wit...

Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to com...

The analysis of handwriting and drawing has been adopted since the early studies to help diagnose neurodegenerative diseases, such as Alzheimer’s and Parkinson’s. Departing from the current state-of-the-art methods that approach the problem of discriminating between healthy subjects and patients by using two- or multi-class classifiers, we propose...

We present a method for discriminating between healthy subjects and Alzheimer’s diseases patients from on-line handwriting. Departing from the current state of the art methods, that adopts machine learning methods and tools for building the classifier, we propose to apply the Negative Selection Algorithm. The major advantage of the proposed method...

The hypothesis that we intend to investigate here is that the extent of the impact of Covid-19 on a given country can be explained starting from a set of indicators and by using machine learning methodologies. The purpose of this chapter is not to find a way to solve the problem in an optimal way. Rather, we aim at performing a preliminary study to...

In the last decades, early disease identification through non-invasive and automatic methodologies has gathered increasing interest from the scientific community. Among others, Parkinson's disease (PD) has received special attention in that it is a severe and progressive neuro-degenerative disease. As a consequence, early diagnosis would provide mo...

This chapter describes the application of a recent compartment-based epidemiological model, namely the SEIAR (Susceptible-Exposed-Infectious-Asymptomatic-Recovered), to estimate the spreading of the coronavirus COVID-19 in Italy and in some of its regions. The model is here extended through the definition of a time-dependent dynamic social distanci...

Time-optimal control of robotic manipulators along specified paths is a well-known problem in robotics. It concerns the minimization of the trajectory-tracking time subject to a constrained path and actuator torque limits. Calculus of variations reveals that time-optimal control is of bang-bang type, meaning that at least one actuator is in saturat...

In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving th...

The aim of this paper consists in the application of a recent epidemiological model, namely SEIR with Social Distancing (SEIR--SD), extended here through the definition of a social distancing function varying over time, to assess the situation related to the spreading of the coronavirus Covid--19 in Italy and in two of its most important regions, i...

We address the problem of designing a machine learning tool for the automatic diagnosis of Parkinson’s disease that is capable of providing an explanation of its behavior in terms that are easy to understand by clinicians. For this purpose, we consider as machine learning tool the decision tree, because it provides the decision criteria in terms of...

Deep Neural Networks (DNNs) may be very effective for the classification over highly-sized data sets, especially in the medical domain, where the recognition of the occurrence of a specific event related to a disease is of high importance. Unfortunately, DNNs suffer from the drawback that a good set of values for their configuration hyper-parameter...

Problems involving large-scale global optimization (LSGO) are becoming more and more frequent. For this reason, the last few years have seen an increasing number of researchers interested in improving optimization metaheuristics in such a way as to cope effectively with high-dimensional search domains. Among the techniques to enhance scalability, o...

This paper illustrates the development and the applicability of an Evolutionary Computation approach to enhance the treatment of Type-1 diabetic patients that necessitate insulin injections. In fact, being such a disease associated to a malfunctioning pancreas that generates an insufficient amount of insulin, a way to enhance the quality of life of...

Background: The use of Artificial Intelligence (AI) systems for automatic diagnoses is increasingly in the clinical field, being a useful support for the identification of several diseases. Nonetheless, the acceptance of AI-based diagnoses by the physicians is hampered by the black-box approach implemented by most performing systems, which do not c...

Early disease identification through non-invasive and automatic techniques has gathered increasing interest by the scientific community in the last decades. In this context, Parkinsons disease (PD) has received particular attention in that it is a severe and progressive neurodegenerative disease and, therefore, early diagnosis would provide more prom...

Within this paper a general-purpose distributed evolutionary algorithm is presented, and is applied to the pair-wise registration of range images. Registration is carried out by utilizing the Grid Closest Point (GCP) for the graphical registration operations and the distributed algorithm to search for the best possible transformation of a scene ima...

This paper describes our preliminary steps towards the deployment of a brand-new original feature for a telemedicine portal aimed at helping people suffering from diabetes. In fact, people with diabetes necessitate careful handling of their disease to stay healthy. As such a disease is correlated to a malfunction of the pancreas that produces very...

In this paper an improved version of a general-purpose asynchronous adaptive multi-population model for distributed Differential Evolution algorithm is investigated. Specifically, in addition to an asynchronous mechanism for a multi-population recombination employed to exchange information, the distributed algorithm is endowed also with an innovati...

In recent years, research on large scale global optimization (LSGO) provided metaheuristics able to effectively tackle real-valued objective functions depending on thousand of variables. Nevertheless, finding a suitable solution of LSGO problems often requires a significantly high number of fitness evaluations. Therefore, when the objective functio...

In high-power-density power electronics application, it's important to be able to predict the power losses of semiconductor devices in order to maximize global system efficiency and to avoid thermal damages of the components. In this paper a novel approach to model the power losses of Insulate Gate Bipolar Transistors (IGBT) in Induction Cooking (...

This paper discusses the identification of Ferrite Core (FC) power inductors parameters in the real operating conditions relevant to Switch-Mode Power Supplies starting from experimental measurements. A novel method for parameters identification is proposed, based on Evolutionary Algorithms (EAs) and on the analysis of inductors non-linear behavior...

We introduce a multiple classifier system that incorporates an Evolutionary Algorithm for dynamically selecting the set of classifiers to be included in the pool. The proposed technique is applicable when the classifiers provide both the class assigned to the input sample and a measure of thereliability of the classification. For each sample, the e...

This paper presents an adaptive model for automatically pair–wise registering range images. Given two images and set one as the model, the aim is to find the best possible spatial transformation of the second image causing 3D reconstruction of the original object. Registration is effected here by using a distributed Differential Evolution algorithm...

Migration topology plays a key role in designing effective distributed evolutionary algorithms. In this work we investigate the impact of several network topologies on the performance of a stepping–stone structured Differential Evolution model. Although some issues on the control parameters of the migration process and the way they affect the effic...

Proceedings of EvoApplications 2014.

This paper presents a method for automatically pair---wise registering range images. Registration is effected adding chaos to a Differential Evolution technique and by applying the Grid Closest Point algorithm to find the best possible transformation of the second image causing 3D reconstruction of the original object. Experimental results show the...

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The 65 revised full papers presented were carefully reviewed and selected from 119 su...

This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoCOP, EvoBIO, and EvoMUSART. The 65 revised full papers presented were carefully reviewed and selected from 119 su...

Migration strategy plays an important role in designing effective distributed evolutionary algorithms. In this work, a novel migration model inspired to the phenomenon known as biological invasion is devised. The migration strategy is implemented through a multistage process involving invading subpopulations and their competition with native indivi...

Migration strategy plays an important role in designing effective distributed evolutionary algorithms. Here, a novel migration model inspired to the phenomenon known as biological invasion is adopted. The migration strategy is implemented through a multistage process involving large invading subpopulations and their competition with native individu...

The use of niching methods for solving real world optimization problems is limited by the difficulty to obtain a proper setting of the speciation parameters without any a priori information about the fitness landscape. To avoid such a difficulty, we propose a novel method, called Adaptive Species Discovery, that removes the basic assumption of perf...

We employ the variational theory of optimal control problems and evolutionary algorithms to investigate the form finding of min-imum compliance elastic structures. Mathematical properties of ground structure approaches are discussed with reference to ar-bitrary collections of structural elements. A numerical procedure based on a Breeder Genetic Alg...

Computational grids assemble multisite and multiowner resources and represent the most promising solutions for processing distributed computationally intensive applications, each composed by a collection of communicating tasks. The execution of an application on a grid presumes three successive steps: the localization of the available resources tog...

In recent years a new view of evolutionary dynamics has emerged based on both neutrality and balance between adaptation and exaptation. Differently from the canonical adaptive paradigm where the genotypic variability is strictly related to the change at fitness level, such a paradigm has raised awareness of the importance of both selective neutrali...

In this chapter, a parallel software system based on differential evolution for the registration of images is designed, implemented and tested on a set of 2-D images in two different fields, i.e. remote sensing and medicine. Two different problems, i.e. mosaicking and changes in time, are faced in the former application field. Registration is carri...

To execute large scale applications exploiting the unemployed aggregated power available on grid nodes, effective and efficient mapping algorithms must be designed. Since the problem of optimally mapping is NP--complete, heuristic techniques can be profitably adopted to find near--optimal solutions. Here a multiobjective Differential Evolution algo...

An innovative strategy, based on Extremal Optimization, to map the tasks making up a user application in grid environments is proposed. Differently from other evolutionary–based methods which simply search for one site onto which deploy the application, our method deals with a multisite approach. Moreover, we consider the nodes composing the sites...

The issue of automatically recognizing digitalized human–made hand signs is a crucial step in facing human–computer interaction and is of paramount importance in fields such as domotics. In this paper Differential Evolution is used to perform classification of hand signs collected in a reduced version of the Auslan database. The performance of the...

Extremal Optimization is proposed to map the tasks making up a user application in grid environments. To comply at the same time with minimal use of grid resources and maximal hardware reliability, a multiobjective version based on the concept of Pareto dominance is developed. The proposed mapper is tested on eight different experiments representin...

A software system grounded on Differential Evolution to automatically register multiview and multitemporal images is designed, implemented and tested through a set of 2D satellite images on two problems, i.e. mosaicking and changes in time. Registration is effected by looking for the best affine transformation in terms of maximization of the mutual...

Automatic recognition of hand gestures is a crucial step in facing human–computer interaction. Differential Evolution is used to perform automatic classification of hand gestures in a thirteen–class database. Performance of the resulting best individual is computed in terms of error rate on the testing set, and is compared against those of other te...

Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in th...

We introduce a multiple classifier system for dynami-cally selecting the set of experts to be included in the pool. The proposed technique is applicable when the experts provide both the class assigned to the input sample and a measure of the reliability of the classification, and is based on the analysis of the relationship between the experts to...

In this paper a Genetic Programming algorithm based on Solomonoff probabilistic induction concepts is designed and used to face an Inductive Inference task, i.e. symbolic regression. To this aim, Schwefel function is dressed with increasing levels of additive noise and the algorithm is employed to denoise the resulting function and recover the star...

The 11th European Conference on Genetic Programming, EuroGP 2008, took place in Naples, Italy from 26 to 28 March in the University of Naples Congress Centre with spectacular views over the Gulf of Naples. This volume contains the papers for the 21 oral presentations and 10 posters that were presented during this time. A diverse array of topics wer...

Effective and efficient mapping algorithms for multisite parallel applications are fundamental to exploit the potentials of grid computing. Since the problem of optimally mapping is NP-complete, evolutionary techniques can help to find near-optimal solutions. Here a multiobjective Differential Evolution is investigated to face the mapping problem i...

Grid systems, constituted by multisite andmulti-owner timeshared resources, make a great amount of locally unemployed computational power accessible to users. To profitably exploit this power for processing computationally intensive grid applications, an efficient multisite mapping must be conceived. The mapping of cooperating and communicating app...

The problem of locating all the optima within a multimodal fitness landscape has been widely addressed in evolutionary computation, and many solutions, based on a large variety of different techniques, have been proposed in the literature. Among them, fitness sharing (FS) is probably the best known and the most widely used. The main criticisms to F...

This paper deals with the design and implementation of a software system based on Differential Evolution for the registration of images, and in its testing by means of a set of bidimensional remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in...

The use of Particle Swarm Optimization, a heuristic optimization technique based on the concept of swarm, is described to face the problem of classification of instances in multiclass databases. Three different fitness functions are taken into account, resulting in three versions being investigated. Their performance is contrasted on 13 typical tes...

In this paper a parallel software system based on Differential Evolution for the registration of images is designed, implemented and tested on a set of 2–D remotely sensed images on two problems, i.e. mosaicking and changes in time. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual...

A theory, called the Kinematic Theory of Rapid Human Movement, was proposed a few years ago to analyze rapid human movements, called the Kinematic Theory of Rapid Human Movements, based on a delta-lognormal equation that globally describes the basic properties of the velocity profiles of an end-effector using seven parameters. This realistic model...

This paper deals with the design and implementation of a parallel software system based on differential evolution for the registration of images, and with its testing on two bidimensional remotely sensed images on mosaicking problem. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutua...

Increase in intensive applications with different computational requirements, coupled with the unification of remote and diverse resources thanks to advances in the wide- area network technologies and the low cost of components, have encouraged the development of grid computing. To exploit the promising potentials of geographically distributed reso...

A Genetic Programming algorithm based on Solomonoff's probabilistic induction is designed and used to face an Inductive Inference task, i.e., symbolic regression. To this aim, some test functions are dressed with increasing levels of noise and the algorithm is employed to denoise the resulting function and recover the starting functions. Then, the...

Most of the classical clustering algorithms are strongly dependent on, and sensitive to, parameters such as number of expected clusters and resolution level. To overcome this drawback, a Genetic Programming framework, capable of performing an automatic data clustering, is presented. Moreover, a novel way of representing clusters which provides inte...

In this paper an approach based on genetic programming for forecasting stochastic time series is outlined. To obtain a suitable test-bed some well-known time series are dressed with noise. The GP approach is endowed with a multiobjective scheme relying on statistical properties of the faced series, i.e., on their momenta. Finally, the method is app...

The effects of periodic environmental fluctuations on the adaptive behavior and on the survival chance of a population of individuals are investigated as a function of both the genotypes carried, i.e., haploid or diploid. Only extreme and exogenous changes have been taken into account in order not to complicate the model under investigation. Moreov...

In this paper an innovative approach to Spectral Pattern Recognition for multispectral images based on Genetic Programming is introduced. The problem is faced in terms of unsupervised pixel classification. Given an image consisting in B bands, the goal is to find the optimal number of clusters and the positions of their centres in the B-dimensional...

In this paper a system based on Genetic Programming for forecasting nonlinear time series is outlined. Our system is endowed with two features. Firstly, at any given time t, it performs a τ-steps ahead prediction (i.e. it forecasts the value at time t + τ) based on the set of input values for the n time steps preceding t. Secondly, the system autom...

Differential Evolution, a version of an Evolutionary Algorithm, is used to perform automatic classification of handsegmented image parts collected in a seven–class database. Our idea is to exploit it to find the positions of the class centroids in the search space such that for any class the average distance of instances belonging to that class fro...

In the context of Solomonoff's Inductive Inference theory, Induction operator plays a key role in modeling and correctly predicting the behavior of a given phenomenon. Unfortunately, this operator is not algorithmically computable. The present paper deals with a Genetic Programming approach to Inductive Inference, with reference to Solomonoff's alg...

Particle Swarm Optimization (PSO) is a heuristic optimization technique showing relationship with Evolutionary Algorithms and strongly based on the concept of swarm. It is used in this paper to face the problem of classification of instances in multiclass databases. Only a few papers exist in literature in which PSO is tested on this problem and th...