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LION11 Scope

The 11th Learning and Intelligent OptimizatioN Conference

Practitioners using heuristic algorithms for hard optimization problems are confronted with the burden of selecting the most appropriate method, in many cases through expensive algorithm configuration and parameter tuning . Scientists seek theoretical insights and demand a sound experimental methodology for evaluating algorithms and assessing strengths and weaknesses. This effort requires a clear separation between the algorithm and the experimenter, who, in too many cases, is "in the loop" as a motivated intelligent learning component. LION deals with 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 online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

This meeting, which continues the successful series (1.Andalo, 2.Trento, 3.Trento, 4.Venice, 5.Rome, 6.Paris, 7.Catania, 8.Gainesville, 9.Lille, 10.Ischia), explores the intersections and uncharted territories between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Russia has a long tradition in optimization theory, computational mathematics and "intelligent learning techniques" (in particular cybernetics and statistics). The location of LION11 in Nizhny is an occasion to meet researchers and consolidate research and human links.
In internet time the value of a conference must go well beyond presenting scientific papers. In particular, we are discussing and planning:

  • Contests in continuous global optimization (a future edition of GENOPT ) and in selected discrete optimization problems.
  • Sessions dedicated to commercial software and open source initiatives..
  • Parallel computing, supercomputing, and cloud for "big optimization".
  • Elevator pitches about new and crazy ideas (also by PhD students) to get rapid feedback by senior people.
  • Tutorials about methods and software.

Conference proceedings are published by Springer-Verlag (in its LNCS series). A special issue of a scientific journal with invited extended version of the best papers is being planned.

Important dates

  • Sep 10, 2016 call for proposals of special sessions, contests, industrial sessions (deadline Oct 10,2016)
  • Dec 18 Dec 25, 2016 paper submission deadline.
  • March 10, 2017 paper acceptance notification.
  • June 19-20-21, 2017, conference at Nizhny Novgorod, Russia
  • Collocated with the 7th International Conference on Network Analysis : June 22-23-24, 2017

Download one-page flyer of LION11 in PDF. one-page flyer of LION11 in Word

Plenary Speakers

  • Renato De Leone, Camerino, Italy
         Title: The use of grossone in optimization: a survey and some recent results
         Abstract: In a series of papers [1, 2], Yaroslav Sergeyev recently proposed a novel approach to infinite and infinitesimal numbers whose peculiar characteristic is the attention to numerical aspects and to applications. The numeral system is computationally and practically oriented and it is based on the use of a new infinite number called grossone and introduced as the number of elements of the set, N, of natural numbers. This approach gives the opportunity to execute numerical computations with infinite and infinitesimal numbers in a unique framework with finite quantities using a new positional numeral system with base equal to grossone. In this talk we will discuss some applications of grossone in Mathematical Programming. We will show that the new methodology with infinite and infinitesimal quantities could be used in innovative ways in penalty methods in constrained optimization, conjugate gradient methods, lexicographic multi-objective linear programming, and convex non-smooth optimization.
         Keywords: Mathematical Programming; Nonlinear programming; Grossone
    Related references:   1.   Y.D. Sergeyev. A new applied approach for executing computations with infinite and infinitesimal quantities. Informatica, vol. 19(4), pp. 567-596 (2008)
      2.   Y.D. Sergeyev. Numerical computations and mathematical modelling with in finite and infinitesimal numbers. Journal of Applied Mathematics and Computing. vol. 29, pp. 177-195 (2009).
  • Panos Pardalos, Gainesville, USA
         Title: Quantification of network dissimilarities and its practical implications
         Abstract: In this lecture, we analyze a novel measure that quantifies network dissimilarities by comparing its performance with other well-known tools. The efficacy of this measure, based on Information Theory, depends on the use of rich information extracted from the graphs. We show here that the measure has promising implications in several research areas that include, bioinformatics, climate dynamics, percolation in networks, network robustness and model selection. We perform extensive computational experiments on real and artificial networks. Future research directions, which include applications to multiplex settings, will also be discussed.
    This is joint work with T. Schieber, M.G. Ravetti, and L. Carpi.
    Related references:
      1.   T.A. Schieber, L. Carpi, A. Díaz-Guilera, P.M. Pardalos, C. Masoller, M.G. Ravetti. Quantification of network structural dissimilarities . Nature Communications (2017) Jan 9;8:13928.
      2.   T.A. Schieber, L. Carpi, A.C. Frery, O.A. Rosso, P.M. Pardalos, and M.G. Ravetti. Information Theory Perspective on Network Robustness. Physics Letters A (2016) vol. 380(3), pp. 359-364.
      3.   T.A. Schieber, M.G. Ravetti, P.M. Pardalos. A Review on Network Robustness from an Information Theory Perspective . DOOR 2016, Lecture Notes in Computer Science (2016) vol. 9869, pp. 50-60.
  • Julius Žilinskas, Vilnius, Lithuania
         Title: Deterministic algorithms for black box global optimization
         Abstract: In this lecture we discuss deterministic algorithms for global optimization, where many local minimizers may exist and the objective function may be computable as a black box. A general assumption about the objective function is used that it satisfies the Lipschitz condition. We overview the branch and bound scheme for global optimization and bounds based on the Lipschitz condition. Most often the Lipschitz constant is unknown, for such a case we discuss algorithms inspired by a DIRECT scheme where a set of possible values for the Lipschitz constant is considered. We discuss various shapes of subregions including hyper rectangles and simplices, their advantages and disadvantages. We also investigate and compare bisection and trisection of subregions and various sampling strategies for evaluation of the objective function in the subregions.
  • Nenad Mladenovic, Valenciennes, France
         Title: Less is more approach in Heuristic Optimization
         Abstract: Heuristic approach named ’Less is more approach’ (LIMA) has recently been proposed in Mladenovic et al (2016). Its main idea is to find the minimum number of search ingredients in solving some particular optimization problem that makes some heuristic more efficient than the currently best in the literature. More precisely, the goal is to make heuristic as simple as possible, but in the same time more effective and efficient than the current state-of-the-art heuristic.


  • Adil Erzin, Novosibirsk, Russia
         Title: Some optimization problems in the wireless sensor networks
         Abstract: Wireless sensor networks (WSN) consist of the electronic devices that collect information within a certain coverage area, partially process it and send data to the base station (BS). In the WSN, a critical resource is the energy of sensors, which is spent mainly on monitoring and transmitting data. In this connection, the following three main problems of computational geometry and combinatorial optimization arise.
      1. Since the sensing energy consumption of the sensor is proportional to the area covered by it, the problem of energy-efficient monitoring reduces to the classical problem of constructing the least dense cover.
      2. If the first problem is solved, then the sensors are placed and their parameters are specified, and for the data transmission it is necessary to build an energy-efficient communication network by determining the sensor’s transmitter powers in such a way as to obtain a connected network for the maintenance of which minimum energy is spent. This problem is known as the Min-Power Symmetric Connectivity Problem.
      3. In the WSN, for transmission of data collected by sensors, as a rule, one radio frequency is used and if more than one transmitter is operating in the sensor receiving zone, then (because of the interference of radio waves) a conflict arises. When aggregating data in the BS, in order to save energy, each sensor transmits only once during the aggregation session. This means that in the communication graph, found as a result of solving problem 2, we need to find an aggregation tree (AT) rooted in the BS. Moreover, the sensor cannot simultaneously receive and transmit and cannot receive data packets from more than one sender. It is required to find an AT and a min-length conflict-free aggregation schedule.
      In the tutorial the various statements of the problems 1–3, as well as algorithms for their solution, will be presented.
  • Mario Guarracino, Naples, Italy
         Title: Laplacian-based semi supervised learning
         Abstract: Supervised and unsupervised learning models are powerful tools to interpret data, with the main difference being that the former relies on the prior knowledge of class assignments (i.e. the labels) for a set of initial points, whereas in the latter a model is built without any prior information on the underlying data structure. Supervised algorithms are mostly used to produce predictive models, whereas unsupervised are more effective in characterizing data and uncovering hidden structures. Nevertheless, there is a third possibility, in which labeled and unlabeled data are used together to build models that are both characterizing and predictive. This methodology is known as semi-supervised learning. In this tutorial we will describe some novel semi-supervised techniques, providing some real world examples.

Paper Submission deadline December, 25

Paper Format

Please prepare your paper in English using the Lecture Notes in Computer Science (LNCS) template, which is available here . Papers must be submitted in PDF.

Types of Submissions

When submitting a paper to LION11, authors are required to select one of the following three types of papers:
  • Long paper: original novel and unpublished work (max. 15 pages in LNCS format);
  • Short paper: an extended abstract of novel work (max. 6 pages in LNCS format);
  • Work for oral presentation only (no page restriction; any format). For example, work already published elsewhere, which is relevant and which may solicit fruitful discussion at the conference.

Submission System

All papers must be submitted using EasyChair.


Participants of the conference have to register on the website till April 15.
The conference is organized in the Oka hotel . We recommend to reserve an accommodation in this hotel.
You could select a room in the registration form (please, specify arrival/departure dates). The Organizing Committee will reserve accommodation for you for these dates. Payment can be done at check-in. After April, 15, the Organizing Committee is not responsible for the accommodation for the conference participants.
You could reserve accommodation independently using website of the hotel or service ( Oka 3*, Oka 4* )

Regular registration fee (till March 31 April 15)
  • 300 Euro
  • 220 Euro for master and postgraduate students
Late registration fee (till April 30)
  • 400 Euro
  • 250 Euro for master and postgraduate students
Bank details for registration fee


Chair and Local Chairs

  • General chair: Yaroslav Sergeyev, Head of Numerical Calculus Laboratory, DIMES, University of Calabria (Italy), Lobachevsky University, Nizhny (Russia).
  • Local organization committee: Roman Strongin (Chair), Victor Kasantsev (Vice-Chair), Vadim Saygin (Vice-Chair), Konstantin Barkalov, Lev Afraimovich, Dmitri Balandin, Victor Gergel, Vladimir Grishagin, Dmitri Kvasov, Iosif Meerov, Grigory Osipov, Mihail Prilutskii, Alexadner Sysoyev, Dmitri Shaposhnikov, Ekaterina Goldinova.
  • Steering committee: Roberto Battiti (head), Holger Hoos, Youssef Hamadi, Mauro Brunato, Thomas Stuetzle, Christian Blum, Martin Charles Golumbic, Marc Schoenauer, Xin Yao, Benjamin W. Wah.
  • Technical program committee
  • Annabella Astorino, Roberto Battiti, Bernd Bischl, Christian Blum, Mauro Brunato, Sonia Cafieri, Andre Carvalho, John Chinneck, Andre Augusto Cire, Renato De Leone, Luca Di Gaspero, Bistra Dilkina, Adil Erzin, Giovanni Fasano, Paola Festa, Antonio Fuduli, David Gao, Martin Golumbic, Vladimir Grishagin, Tias Guns, Youssef Hamadi, Frank Hutter, George Katsirelos, Michael Khachay, Oleg Khamisov, Yury Kochetov, Lars Kotthoff, Dmitri Kvasov, Dario Landa-Silva, Hoai An Le Thi, Daniela Lera, Vasily N. Malozemov, Marie-Eleonore Marmion, Silvano Martello, Kaisa Miettinen, Nenad Mladenovic, Evgeni Nurminski, Barry O'Sullivan, Panos Pardalos, Remigijus Paulavicius, Thomas Pock, Mikhail Posypkin, Oleg Prokopyev, Helena Ramalhinho, Massimo Roma, Francesca Rossi, Valeria Ruggiero, Horst Samulowitz, Marc Schoenauer, Meinolf Sellmann, Yaroslav Sergeyev, Carlos Soares, Alexander Strekalovskiy, Thomas Stützle, Eric D. Taillard, Tatiana Tchemisova, Gerardo Toraldo, Michael Trick, Daniele Vigo, Petr Vilím, Luca Zanni, Anatoly Zhigljavsky, Antanas Žilinskas, Julius Žilinskas.

    Location, travel, accommodation

    Nizhny Novgorod, Russia

    Nizhny Novgorod (Russian: Ни́жний Но́вгород), colloquially shortened to Nizhny, is the fifth-largest city in Russia (population of 1,250,000). From 1932 to 1990, it was known as Gorky after the writer Maxim Gorky, who was born there. The city is an important economic, transportation and cultural center, one of the centers of the IT Industry in Russia with twenty-five scientific R&D institutions, home of the N. I. Lobachevsky State University of Nizhny Novgorod which is hosting LION11, and main center of river tourism in Russia. ( Nizhny in wikipedia )

    After the war, Gorky was completely closed to foreigners to safeguard the security of Soviet military research and production facilities. As a curiosity, the dissident Sacharov was exiled to Gorky in 1980. The city was opened again after the dissolution of the Soviet Union in 1990.

    The Nizhny Novgorod International Airport has many flights from the world with affordable cost (about Euro 200-300 in Europe, 600 New York, 1000 Los Angeles, 600 Tokyo, ...). From major European cities, Nizhny can be conveniently reached by plane within 6 to 8 hours. As an alternative, you can travel to one of three Moscow airports (Sheremetevo, Domodedovo, Vnukovo), in some cases with cheaper flights, optionally visit the Kremlin and the Red Square, and then take a modern and fast train (with sleeping possibilities, private shower and restaurant) to Nizhny (about 3 h 30 min). We will place useful links for getting train tickets (with our suggestions) here.

    The climate in June is perfect for a visit. As part of LION11 we plan to organize:

    • A tour of Nizhny with a guide with deep knowledge of the local history (not the usual banal touristic tour! )
    • A boat trip along the inspiring and infinite ( grossone :) Volga and Oka rivers.

    The conference is organized in the Oka hotel . We recommend to reserve an accommodation in this hotel.
    You could select a room in the registration form (please, specify arrival/departure dates). The Organizing Committee will reserve accommodation for you for these dates. Payment can be done at check-in. After April, 15, the Organizing Committee is not responsible for the accommodation for the conference participants.
    You could reserve accommodation independently using website of the hotel or service ( Oka 3*, Oka 4* )

Special Sessions

Focussed sessions at LION11

Special sessions are organized as a way to focus submissions and encourage more interaction between specific communities. In general, submission and publication rules are the same as for the general call for papers, with the organizers of the special sessions coordinating and helping in identifying competent reviewers. Pls direct proposals for special session to the general chair: Yaroslav D. Sergeyev: yaro ((AT)) .

--- Session 0 --- GENOPT Generalization-based contest in global optimization ---
Roberto Battiti, Head of LIONlab for "Machine Learning and Intelligent Optimization", University of Trento (Italy);
Mauro Brunato, LIONlab, University of Trento (Italy);
Yaroslav Sergeyev, Head of Numerical Calculus Laboratory, University of Nizhny Novgorod (Russia); DIMES, University of Calabria (Italy);
Dmitri Kvasov, University of Nizhny Novgorod (Russia); DIMES, University of Calabria (Italy).

While comparing results on benchmark functions is a widely used practice to demonstrate the competitiveness of global optimization algorithms, fixed benchmarks can lead to a negative data mining process. The motivated researcher can "persecute" the algorithm choices and parameters until the final designed algorithm "confesses" positive results for the specific benchmark.

With a similar goal, to avoid the negative data mining effect, the GENOPT contest will be based on randomized function generators, with fixed statistical characteristics but individual variation of the generated instances.

The generators will be made available to the participants to test offline and online tuning schemes, but the final competition will be based on random seeds communicated in the last phase. A dashboard will reflect the current ranking of the participants, who are encouraged to exchange preliminary results and opinions. The final "generalization" ranking will be confirmed in the last competition phase.

Tentative schedule: to be announces Sep 2016, together with new benchmark functions

After LION: special issue of good-quality journal dedicated to results obtained by the reviewed and winning papers. For further information, visit

--- Session 1 --- Large Scale Nonlinear Programming ---
Fasano Giovanni ( fasano ((AT)) )
Roma Massimo ( roma ((AT)) )
Caliciotti Andrea ( caliciotti ((AT)) )

The session proposes a discussion on recent advances of Nonlinear Programming, including promising research topics in both constrained and unconstrained optimization. The contribution of both expert scientists and researchers, as well as practitioners, is welcome. Possible topics for presentations include, but are not limited to:

  • applications of nonlinear optimization,
  • nonlinear programming methods,
  • parallel algorithms and numerical stability,
  • convex and nonconvex optimization over large data sets,
  • interior-point methods.

--- Session 2 --- Computational and Optimization Problems of Efficient Data Collection and Transmission ---
Prof. Adil I. Erzin ( adilerzin ((AT)) )
Dr. Roman V. Plotnikov ( nomad87 ((AT)) )

Data collection and transmission networks, such as wireless sensor networks, are used in many areas of human activity. Elements of such networks often have a limited supply of non-renewable energy, so energy saving issue is the most important.

Problems of the effective functioning of data collection and transmission networks are reduced to such problems of computational geometry and combinatorial optimization as the problem of constructing optimal covers, to the synthesis of energy efficient communications networks, and to the problems of effective data aggregation.

Rational energy consumption can increase the life time of the network at times. Within the framework of a special session is planned to hear the results regarding new problem formulations, as well as algorithms for solving the above problems.

--- Session 3 --- Large Scale Projection Problems ---
Prof. Evgeni Nurminski, Far Eastern Federal University (Russia) (nurmi ((AT)) )

Finding a least-norm element of a certain set is at the heart of many approaches in automatic classification, machine learning, optimization, computational physics, chemistry, and economics. The size of these problems and their complexity continues to grow up exponentially, overcoming hardware improvements in computing machinery. This certainly calls for algorithmic advances and this session invites interested parties to participate in the discussions on specific projection problems and their applications, as well as new ideas for algorithms. A special attention will be given to solution of structured projection problems, involving decomposition and parallel computations.

--- Session 4 --- Stochastic and deterministic global optimization ---
Prof. Anatoly Zhigljavsky, Cardiff University, (Great Britain) (ZhigljavskyAA ((AT)) )
Prof. Vladimir Grishagin, Nizhni Novgorod State University (Russia) (vagris ((AT)) )

Decision making models formulated as global optimization problems occur in many contemporary real world applications and require for their development significant variety of theoretical, algorithmic, computational, and applied techniques. The scope of the session consists of a versatile consideration of theoretical fundamentals and numerical analysis of global optimization problems in various fields: nonlinear, combinatorial, stochastic programming, multicriterial optimization, optimal control, game theory, approximation, etc. As numerical algorithms are the main tools for solving global optimization problems they are considered in the broad sense including presentation of new efficient techniques, their theoretical substantiation and implementation, generalization to parallel computations on supercomputers and to new paradigms of computations, results of numerical experiments, and solving some applied problems.

Contact form

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