Curriculum Docente

Antonio Della Cioppa received the M.Sc. degree in Physics and Ph.D. in Computer Science, both from University of Naples "Federico II", Italy, in 1993 and 1999, respectively. Since then, he has been carrying out research in the field of Artificial Intelligence with particular reference to the Computation Intelligence.

He received a fellowship from the National Research Council of Italy in 1995. From 1999 to 2003, he was a Postdoctoral Fellow at the Department of Information Engineering, Electric Engineering and Applied Mathematics, University of Salerno, Italy. Since 2004, he has been Assistant Professor at the Department of Information Engineering, Electric Engineering and Applied Mathematics, University of Salerno, where he is currently Associate Professor (SSD: ING-INF/05) of Computer Science and Artificial Intelligence and admitted to full professor.

Form 2020 he is external research associate at the Institute for High-Performance Computing and Networking (ICAR), National Research Council of Italy (CNR), while from 2002 to 2004 he acted as research associate at the National Institute for the Physics of Matter (INFM).

His research interests cover several areas of the Artificial Intelligence and Computer Science, mainly related to Machine Learning, Data Mining, Inductive Inference and, in particular, Evolutionary Computation, also using some paradigms of Parallel and Distributed Computing, Prebiotic Evolution, Darwinian Dynamics, Speciation Models. In particular, Antonio Della Cioppa research activity focuses on the relation between Nature and Computation from two diverse yet complementary and strongly interleaved perspectives:

  • developing nature-inspired models and techniques for solving complex problems;
  • using computational models and techniques for simulating natural processes and organisms.

Within the first perspective, the research focuses on developing speciation models in evolutionary algorithms for solving multimodal optimization problems and on dynamical architecture of neural networks for solving complex tasks. In modeling natural evolution, the main focus in on methods for promoting species formation and maintenance in evolutionary algorithms. Such methods apply whenever there are resources, either implicit or explicit, that can be shared among individuals in the population and find application in solving multi-objective optimization problems, as well as in machine learning.

As regards neural networks, much of recent machine learning has focused on deep learning. In this context, the research focuses on the alternative approach of Neuroevolution, which harnesses evolutionary algorithms to optimize neural networks, in that it enables important capabilities that are typically unavailable to gradient-based approaches, including learning neural network building blocks such as activation functions, hyperparameters, architectures and even the algorithms for learning themselves. Neuroevolution also differs from deep learning (and deep reinforcement learning) by maintaining a population of solutions during search, enabling extreme exploration and massive parallelization. The aim is to design new techniques that should be effective in other machine learning areas too.

Within the latter perspective, investigation focuses on the study and development of evolutionary computation models based on self-organizing evolutionary dynamics aiming at creating explicit models interpretable for the final user. In both cases, the research activities aim at developing intelligent systems for real world applications such as time series prediction, multivariable and multi-objective optimization and simulation of complex systems.

Antonio Della Cioppa has been among the first researchers to propose and design Evolutionary Computation methodologies to the aerodynamic design of wing profiles and to Kolmogorov/Chaitin algorithmic complexity for the inference induction problem. On the former, he contributed to an International Cooperation Agreement between the CNR and von Karman Institute for Fluid Dynamics, Rhode Saint Genese, Belgium, while on the latter he collaborated with prof. Ray Solomonoff, the father of the General Theory of Inductive Inference and was a founder of algorithmic information theory and an originator of the branch of artificial intelligence based on machine learning, prediction and probability.

The whole impact of his research is assessed by the following indicators, as of December 16, 2020:

  • Google Scholar: H-index: 24, citations: 2203, i10-index: 48, publications: >100;
  • Scopus: H-index: 19, citations: 1281, publications: 97;
  • Web of Science: H-index: 14, citations: 645, publications: 64.

He published more than one-hundred papers on international journals, scientific books and proceedings. Top articles have been published in top international refereed journals, books and conferences including IEEE Transactions on Evolutionary Computation (JCR IF 2019: 11.169), Patten Recognition (JCR IF 2019: 7.196), Future Generation Computer Systems (JCR IF 2019: 6.125), Information Sciences (JCR IF 2019: 5.910), Journal of Network and Computer Applications (JCR IF 2019: 5.570), Applied Soft Computing (JCR IF 2019: 5.472), Recognition Letters (JCR IF 2019: 3.255), Soft Computing (JCR IF 2019: 3.050), Knowledge and Information Systems (JCR IF 2019: 2.936), Journal of Parallel and Distributed Computing (JCR IF 2019: 2.296), Journal of Intelligent and Robotic Systems (JCR IF 2019: 2.259). The most cited paper counts 204 citations and there are more than 13 papers authored by Antonio Della Cioppa having more than 40 citations.

He developed and has been participating as scientific responsible for research units in several scientific projects related to Artificial Intelligence and funded by involving public and private agencies, as well as public institutions. Currently, Antonio Della Cioppa is the scientific coordinator of the “SmartCGM” Cooperation project among the Natural Computation Lab, DIEM, University di Salerno, the Computational Intelligence Lab of the Institute for High Performing Computing and Networking (ICAR), CNR and the Laboratory for Medical Informatics, NTIS Research Centre & the Department of Computer Science, University of West Bohemia (CZ). The team work on the development of a physiological model of glucose dynamics that physicians can use with the aim to personalize treatments of Type 1 Diabetes Mellitus patients.

Antonio Della Cioppa acts as an Editorial Board member for the international journals: Applied Soft Computing (Elsevier), Frontiers in Robotics and AI, Computational Intelligence (Frontiers), The Open Bioinformatics Journal (Bentham), Journal of Artificial Evolution and Applications (Hindawi, 2008–2010). Moreover, he is Associate Editor for the international journals: Evolutionary Intelligence (Springer), Algorithms (MDPI). He was also co-editor for seven volumes of the Lecture Notes in Computer Science (Elsevier) within 2008 and 2016.

He has been on the organizing committee of many important international conferences and workshops, and events such as: EvoStar, The Leading European Event on Bio-Inspired Computation (form 2008), EvoComNet workshop (2013-2016), ACM NEWK Workshop (2020), ACM PDEIM Workshop (from 2018), IEEE MHAEC Special Session (2017). Moreover, he is member of the program committee of many relevant international conferences such as: Genetic and Evolutionary Computation Conference, IEEE Congress on Evolutionary Computation, Parallel Problem Solving from Nature, International Joint Conference on Computational Intelligence, European Conference on Artificial Intelligence, EvoStar-The Leading European Event on Bio-Inspired Computation. He is also referee for many relevant international journals: IEEE ACCESS, IEEE Press, IEEE Transactions on Evolutionary Computation, Evolutionary Computation, Genetic Programming and Evolvable Machines, Information Sciences, Applied Soft Computing, Swarm and Evolutionary Computation, Soft Computing, Journal of Computer Science and Technology, Pattern Recognition Letters, Future Generation Computer Systems, Computers in Biology and Medicine.

Antonio Della Cioppa has been invited speaker in several international conferences and scientific meetings. Some lectures are reported in the following:

  • von Karman Institute for Fluid Dynamics (Bruxelles, Belgium), VKI Colloquia Series, “Heuristic Optimization Techniques and their applications” (1995),
  • Workshop on Artificial Life, “Evolutionary Algorithms and Kolmogorov Complexity” (2003),
  • Workshop on Artificial Life, “A model of Biological Evolution under environmental variations” (2005),
  • Workshop on Artificial Life "Inductive Inference on Noisy Data by Genetic Programming" (2006),
  • Workshop on Artificial Life and Evolutionary Computation, "Machine Learning: Genetic Programming Approach to Inference Induction” (2007),
  • VI International Theras Day, “ExplaInable IA and its Application to Diabetes” (2020).

He is member of the Association for Computing Machinery, the IEEE Computational Intelligence Society and the ACM Special Interest Group on Genetic and Evolutionary Computation, IEEE Computer Society, IEEE Computational Intelligence Society Task Force on Evolutionary Computer Vision and Image Processing, Italian Group for Informatics Engineering (GII), EU Pervasive Adaptation Research Network for Future and Emergent Technologies (2008-2011), European Network of Excellence in Evolutionary Computation (1997-2002).

Antonio Della Cioppa received the Best Paper Award at EvoIASP workshop (2013) for the paper “Adding chaos to Differential Evolution for Range Image Registration” and the Best Presentation Award al 32nd IEEE Symposium on Computer-Based Medical System (2019) - Special Track on Artificial Intelligence for Healthcare: from black box to explainable models for the paper “Automatic Diagnosis of Parkinson Disease through handwriting analysis: a Cartesian Genetic Programming approach”.