[agents] CALL FOR PAPERS: Learning and Intelligent OptimizatioN Conference
Gleb Polevoy - EWI
g.polevoy at tudelft.nl
Wed Dec 20 13:52:10 EST 2017
CALL FOR PAPERS
Learning and Intelligent OptimizatioN Conference
LION 12, Kalamata, Greece, June 10-15, 2018
http://www.caopt.com/LION12/
Scope of the conference
The large variety of heuristic algorithms for hard optimization problems
raises numerous interesting and challenging issues. Practitioners are
confronted with the burden of selecting the most appropriate method, in
many cases through an expensive algorithm configuration and parameter
tuning process, and subject to a steep learning curve. Scientists seek
theoretical insights and demand a sound experimental methodology for
evaluating algorithms and assessing strengths and weaknesses. A
necessary prerequisite for this effort is a clear separation between the
algorithm and the experimenter, who, in too many cases, is "in the loop"
as a crucial intelligent learning component. Both issues are related to
designing and engineering ways of "learning" about the performance of
different techniques, and ways of using past experience about the
algorithm behavior to improve performance in the future. Intelligent
learning schemes for mining the knowledge obtained from different runs
or during a single run can improve the algorithm development and design
process and simplify the applications of high-performance optimization
methods. Combinations of algorithms can further improve the robustness
and performance of the individual components provided that sufficient
knowledge of the relationship between problem instance characteristics
and algorithm performance is obtained.
This meeting, which continues the successful series of LION events (see
LION 5 at Rome, and LION 6 at Paris, and LION 7 at Catania), is aimed at
exploring the intersections and uncharted territories between machine
learning, artificial intelligence, mathematical programming and
algorithms for hard optimization problems. The main purpose of the event
is to bring together experts from these areas to discuss new ideas and
methods, challenges and opportunities in various application areas,
general trends and specific developments. We are excited to be bringing
the LION conference in Greece for the first time.
Proceedings will be published by Springer-Verlag**in Lecture Notes in
Computer Science
Revised selected papers of LION 12 will be published in a special issue
of Annals of Mathematics and Artificial Intelligence
<https://link.springer.com/journal/10472>
Important Dates
January 15, 2018: Paper submission (N.B.: NO other extensions will be
granted!)
February 15, 2018: Author Notification
February 28, 2018: Camera ready for pre-proceedings sent to
cao(at)caopt(dot)com
March 1, 2018: Registration opens
June 10-15, 2018: Conference
Conference General Chairs:
Prof. Panos Pardalos, Center for Applied Optimization, University of
Florida (USA) and
Prof. Ilias Kotsireas , CARGO Lab, Wilfrid Laurier University (Canada)
Special Sessions
How machine learning is revolutionizing healthcare
Organizers:
Dr. Kostas Chrisagis <kostasnc at gmail.com>, City University London,
United Kingdom
Dr. Serafeim Moustakidis <smoustakidis at gmail.com>, Center for
Research and Technology Hellas
Description:
The proliferation of massive and heterogeneous health-related data
brings with it a series of special challenges enabling at the same time
opportunities for improving healthcare. Clinicians and health experts
are overwhelmed by the volume, velocity and variety of the available
data including medical imagery, data from wearable sensors, electronic
health records, genomic data, behavioral and environmental data. The
increased availability of data and computational power has led to a
resurgence of machine learning leading the efforts to transform the vast
amount of complex health-related data into actionable knowledge. Machine
learning and deep learning are now attempting to revolutionize the whole
healthcare sector by improving diagnostics, predicting outcomes, and
changing the way doctors think about providing care. Reflecting this
excitement, this special session aims to identify opportunities and
challenges of the growing intersection of machine learning and health.
Topics of interest include but are not limited to:
Imaging related decision making and computer-aided diagnosis
Multi-modal Clinical Decision Support
Machine learning / deep learning for medical image analysis
Early detection and diagnosis of diseases
Big data analytics in healthcare
Data mining with interpretable models
Enhanced imaging diagnostics
Behavioral analysis with wearables
Variable selection over high dimensional heath related data
Personalized diagnosis and treatment
Drug Discovery using unsupervised learning
Computational Methods in Molecular Biology
---
Computational Intelligence for Smart Cities
Organizers:
Enrique Alba (Professor), University of Malaga, Spain (link)
Konstantinos Parsopoulos (Associate Professor), University
of Ioannina, Greece (link)
Description::)
Global urbanization is continuously reshaping our world. More than half
of the world's population is currently living in urban areas, with
predictions adding 2.5 billion people to the cities over the next few
decades. This transformation provides great opportunities for cultural
and economic growth. However, it also comes along with a number of
challenging problems such as overpopulation in metropolitan areas, cost
of living, environmental pollution, and inadequate infrastructures,
among others. Smart cities attempt to provide solutions to the
continuously growing needs by integrating information technologies and
interconnected devices in urban environments. This allows the collection
and interpretation of huge amounts of data that are used for optimizing
various aspects of the cities operation through the design and modeling
of ad hoc solutions and systems. Smart transportation systems, smart
buildings, smart communications and energy networks are some of the
most active research areas in this domain.
Computational intelligence (C.I.) has played a significant role in most
complex systems existing till now, and they are also expected to have a
prominent position in smart cities. Its constituent methodologies such
as machine learning, data science, artificial neural networks,
evolutionary algorithms, swarm intelligence, and fuzzy logic offer
computationally efficient methodologies for modeling, analyzing, and
optimizing smart cities systems. Indeed, computational intelligence is
one important way to build to the “smart” part of the city. The
interplay of such approaches with operations research and many other
domains (civil engineering, urban planning, policy makers, companies...)
can offer innovative and sustainable solutions to problems of high
complexity. So far, computational intelligence methodologies reckons a
large number of applications in smart cities, including smart
transportation systems, smart logistics, smart energy grids, smart
resources integration and pollution monitoring.
The present special session welcomes works on any aspect of
computational intelligence in smart cities environments, both
theoretical and applied, including:
Computational intelligence in smart transportation and logistics
Computational intelligence in urban mobility and planning
Computational intelligence in smart energy systems
Computational intelligence in sustainability (environmental,
social, economic)
Computational intelligence in smart homes and Internet of
Things
Computational intelligence in smart healthcare systems
Computational intelligence in governance
Computational intelligence for people and good living
Computational intelligence to tourism and entertainment in
the city
Computational intelligence in circular economy
Cyberphysical systems and Internet of Things coupled with C.I.
Computational intelligence for security, big data, open
data, and software for cities
Applications involving efficient learning and optimization methodologies
for this type of problems are strongly encouraged.
Important dates:
February 15, 2018: Paper submission
February 28, 2018: Author Notification
March 15, 2018: Camera ready
June 10-15, 2018: Conference
---
On the borderline between Data Analysis and Combinatorial Optimisation:
models, algorithms, and bounds
Organizers:
Prof. Alexander Kelmanov, Sobolev Institute of Mathematics,
Novosibirsk, Russia
Prof. Michael Khachay, Krasovsky Institute of Mathematics and
Mechanics, Ekaterinburg, Russia
Description:
Combinatorial optimization and data analysis appear to be extremely
close fields of the modern computer science. For instance, various areas
in machine learning: PAC-learning, boosting, cluster analysis, feature
and model selection, etc. are continuously presenting new challenges for
designers of optimization methods due to the steadily increasing demands
on accuracy, efficiency, space and time complexity and so on. In many
cases, learning problem can be successfully reduced to the appropriate
combinatorial optimization problem: max-cut, k-means, p-median, TSP, and
so on. To this end, all the results obtained for the latter problem
(approximation algorithms, polynomial-time approximation schemes,
approximation thresholds) can find their application in design the
high-precision and efficient learning algorithms for the former one. On
the other hand, there are known examples, where combinatorial
optimisation and computational geometry benefits from using approaches
developed in statistical learning theory. Among them are Chernoff like
measure concentration theorems employed for designing of randomised
algorithms and schemas and Bronnimann-Goodrich epsilon-net approach to
approximation the famous Hitting Set problem. This session welcomes
papers presenting new results on computational and parametric
complexity, design and implementation of efficient algorithms and
schemes for various extremal problems coming from combinatorial
optimisation, classification, clustering, computational geometry, and so on.
Topics of interest include but are not limited to:
computational and parametric complexity
inapproximability issues and approximation thresholds
polynomial time solvable subclasses of intractable problems
polynomial time approximation algorithms and schemes
randomized approximation and asymptotically optimal algorithms
efficient approximation algorithms for geometric settings of
NP-hard problems
efficient techniques of supervised, semi-supervised, and
unsupervised learning
Important dates:
February 15, 2018: Paper submission
February 28, 2018: Author Notification
March 15, 2018: Camera ready
June 10-15, 2018: Conference
---
Graphical model selection and applications
Organizers:
Dr. Valeriy Kalyagin, Laboratory of Algorithms and Technologies for
Network Analysis, National Research University Higher School of
Economics, Nizhny Novgorod, Russia
Dr. Mario Guarracino, High Performance Computing and Networking
Institute, Italian National Research Council, Naples, Italy
Description:
Graphical models provide a unifying framework for capturing dependencies
in complex systems. Graphical models are recognized as a useful tool in
many applied fields, such as bioinformatics, communication theory,
combinatorial optimization, signal and image processing, information
retrieval, stock market network analysis and statistical machine
learning. Graphical model selection is a practical problem of
identification of the underlying graphical model from observations. The
session will be devoted to theoretical aspects and practical algorithms
for graphical model selection and its applications. Estimating the graph
structure given a set of observations at the nodes is very common in
many fields and in particular in biology, where the complexity of
processes and functions are widely modeled by networks. From protein
interaction to metabolic pathways, from gene regulatory circuits to
brain connectomes, networks have sizes that range from few thousands to
many trillions vertices. From their analysis, we can obtain more
insights in complex questions, identifying for example their critical
points, robustness and modularity. In this session, we will address some
of the recent advances on graphical model selection, that can find
application in different disciplines and applications.
Topics of interest include but are not limited to:
Graphical model selection in bioinformatics
Graphical model selection in communication
Graphical model selection in combinatorial optimization
Graphical model selection in signal and image processing
Graphical model selection in information retrieval
Graphical model selection in market network analysis
Graphical model selection in statistical machine learning
Graphical model selection in gene expression network
Graphical model selection in gene co expression network
Important dates:
February 15, 2018: Paper submission
February 28, 2018: Author Notification
March 15, 2018: Camera ready
June 10-15, 2018: Conference
---
Optimization and Management in Smart Manufacturing
Organizers:
Dr. Panos M. Pardalos, <pardalos at ufl.edu>, University of Florida, USA
Dr. Xinbao Liu, <lxb at hfut.edu.cn>, School of Management, Hefei
University of Technolog, China
Dr. Jun Pei, <peijun at hfut.edu.cn>, School of Management, Hefei
University of Technolog, China
Description:
Current global science and technology innovation shows some new
development trends and characteristics. Emerging information technology
such as internet and big data technology are widely permeated which
drives the group technology revolution characterized by green,
intelligent, and ubiquitous in almost all the areas. Intelligentization,
greenization, servitization, and interconnection are becoming important
for the development and revolution of manufacturing industry.
Interdisciplinary and networked innovative platform is reforming the
innovation system of traditional manufacturing industry. A new green
manufacturing system based on the value network is forming. This session
aims to apply data-driven resource management and optimization technique
to offer theory support for the management revolution, business pattern
revolution, decision theories and methods innovation, and intelligent
decision system construction.
Topics of interest include but are not limited to:
Network Manufacturing
Sustainability Manufacturing Strategy Management
Sustainability Supply Chain Operations Management
R&D project management of high-end equipment
Environmental and Sustainability Assessment
Operations Management of Smart Factory
Behavioral Operations Management
Production engineering management of high-end equipment
Inventory Planning and Control of Green Products
Green Logistics Operation and Management
Service engineering management of high-end equipment
Remanufacturing engineering management of high-end equipment
Quality Management Based on Industrial Big Data
Development Management of Renewable Energy Technologies
Big Data Applications in Smart Manufacturing
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