Antonio Mucherino's Research
To maintain a webpage detailing ongoing research is very timeconsuming. Every single discovery,
or even a single novel idea, makes its content ancient and a new revision is necessary.
Even scheduling updates every time a new article of mine appears requires considerable efforts,
which I finally prefer to devote to research.
In a smaller scale, I'm currently trying to maintain the section of my curriculum vitae
where I'm used to give some details about my current research. My curriculum vitae can be
download through this webpage.
In brief, my research interests mainly spread over the following three subjects:

Distance Geometry.
The Distance Geometry Problem (DGP) asks whether a simple weighted undirected graph
G=(V,E,d) can be embedded in a Kdimensional Euclidean space so that the
distance between every pair of embedded vertices u and v belonging to V
corresponds to the weight d(u,v) assigned to the edge (u,v), when available.
The DGP is NPhard and has several applications; one of the most interesting is the one in
the field of biology where vertices are atoms of a molecule, and the embedding of G
corresponds to suitable threedimensional conformations of the molecule.
Over the last years, in collaboration with
Leo Liberti,
Carlile Lavor and
Nelson Maculan
and other people, we have been working on the discretization of the DGP.
In 2013, I coedited a
book
devoted to the DGP and its applications; moreover, a
special issue
of
DAM
is about to appear.
A software tool called MDjeep,
which is distributed under the GNU General Public License (v.2),
is under development. It implements a branchandprune framework for discretizable DGPs
and its sources can be downloaded from this
webpage.

Data Mining.
Data mining is the nontrivial extraction of previously unknown, potentially useful
and reliable patterns from a given set of data. Data mining techniques can be mainly
divided into two categories: classification techniques and clustering techniques.
I'm coauthor, with
Panos Pardalos
and
Petraq Papajorgji,
of the textbook
Data Mining in Agriculture.
The book is devoted to data mining techniques applied to the agricultural field.
Both classification and clustering techniques are presented in the book, and many examples of
applications in agriculture and related fields are discussed. The implementations in
Matlab
of simple algorithms help the reader in understanding the topics and allow to perform simple experiments.
My research in data mining is devoted to consistent biclustering for supervised classification
and feature selection.

Metaheuristics.
The flexibility in the conception and use of metaheuristics opens to
a wide domain of development and applications.
My first experience with metaheuristics dates back to my PhD in Italy, where I was working on a
geometrical model for the simulation of protein conformations. At that time, I had implemented
a simple Simulated Annealing, with some adhoc perturbations inspired by the problem at hand.
Then, during my postdoc at University of Florida, I worked on a novel metaheuristic framework that
we called Monkey Search (see below). More recently, with other colleagues, I proposed the
simulation of an environment in evolutionary algorithms. So far, we tried this novel idea
together with Ant Colony Optimization.
Additional information can be found in my
curriculum vitae.
Here below, some short research notes that I have written, over the last years, for this webpage.
They do not cover my whole research work.
