International Workshop on Optimization and Learning (OLA 2018)

Organized by El-Ghazali Talbi and Peter Korošec

Alicante, 26-28 Feb 2018

The workshop OLA'2018 will provide an opportunity to the international research community in optimization and learning to discuss recent research results and to develop new ideas and collaborations in a friendly and relaxed atmosphere. OLA'2018 welcomes presentations that cover any aspects of optimization and learning research such as new high-impact applications, parameter tuning, 4th industrial revolution, new research challenges, hybridization issues, optimization-simulation, meta-modeling, high-performance and exascale computing, surrogate modeling, multi-objective optimization, optimization for machine learning, machine learning for optimization.

Workshop on Real-Parameter Black-Box Optimization Benchmarking (BBOB 2017)

Organized by Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Dejan Tušar, and Tea Tušar

Genetic and Evolutionary Computation Conference (GECCO 2017)

Berlin, 15-19 July 2017

Quantifying and comparing the performance of optimization algorithms is a difficult and tedious task to achieve---but ubiquitous when designing and applying numerical optimization algorithms.

The Black-Box-Optimization Benchmarking (BBOB) methodology associated to the BBOB-GECCO workshops has become a well-established standard for benchmarking stochastic and deterministic continuous optimization algorithms in recent years. A substantial portion of its success can be attributed to the Comparing Continuous Optimization benchmarking platform (COCO) that automatically allows algorithms to be benchmarked and performance data to be visualized effortlessly.

Workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC 2017)

Organized by David Walker, Richard Everson, Jonathan Fieldsend, Bogdan Filipič, and Tea Tušar

Genetic and Evolutionary Computation Conference (GECCO 2017)

Berlin, 15-19 July 2017

Building on workshops held annually since 2010, the eighth annual workshop on Visualisation Methods in Genetic and Evolutionary Computation (VizGEC), to be held at GECCO 2017 in Berlin, is intended to explore, evaluate and promote current visualisation developments in the area of genetic and evolutionary computation (GEC). Visualisation is a crucial tool in this area, providing vital insight and understanding into algorithm operation and problem landscapes as well as enabling the use of GEC methods on data mining tasks. Particular topics of interest are:

  • visualisation of the evolution of a synthetic genetic population
  • visualisation of algorithm operation
  • visualisation of problem landscapes
  • visualisation of multi-objective trade-off surfaces
  • the use of genetic and evolutionary techniques for visualising data
  • novel technologies for visualisation within genetic and evolutionary computation
  • visual steering of algorithms
  • visualisation in real-world applications

As well as allowing us to observe how individuals interact, visualising the evolution of a synthetic genetic population over time facilitates the analysis of how individuals change during evolution, allowing the observation of undesirable traits such as premature convergence and stagnation within the population. In addition to this, by visualising the problem landscape we can explore the distribution of solutions generated with a GEC method to ensure that the landscape has been fully explored. In the case of multi- and many-objective optimisation problems this is enhanced by the visualisation of the trade-off between objectives, a non-trivial task for problems comprising four or more objectives, where it is necessary to provide an intuitive visualisation of the Pareto front to a decision maker. All of these areas are drawn together in the field of interactive evolutionary computation, where decision makers need to be provided with as much information as possible since they are required to interact with the GEC method in an efficient manner, in order to generate and understand good solutions quickly.

In addition to visualising the solutions generated by a GEC process, we can also visualise the processes themselves. It can be useful, for example, to investigate which evolutionary operators are most commonly applied by an algorithm, as well as how they are applied, in order to gain an understanding of how the process can be most effectively tuned to solve the problem at hand. Advances in animation and the prevalence of digital display, rather than relying on the paper-based presentation of a visualisation, mean that it is possible to use visualisation methods so that aspects of an algorithm's performance can be evaluated online.

GEC methods have also recently been applied to the visualisation of data. As the amount of data available in areas such as bioinformatics increases rapidly, it is necessary to develop methods that can visualise large quantities of data; evolutionary methods can, and have, been used for this. Work on visualising the results of evolutionary data mining is also now appearing.

All of these methods benefit greatly from developments in high-powered graphics cards and work on 3D visualisation, largely driven by the computer games community. A workshop provides a good environment for the demonstration of such methods. As well as presenting the results of a GEC process in a traditional visual way, we are also keen to solicit work on other forms of presentation.

International Workshop on Parallel Optimization using/for Multi and Many-core High Performance Computing (POMCO 2016)

Organized by Albert Y. Zomaya, Nouredine Melab, and Imen Chakroun

International Conference on High Performance Computing & Simulation (HPCS 2016)

Innsbruck, 18-22 July 2016

On the road to exascale, multi-core processors and many-core accelerators/coprocessors are increasingly becoming key-building blocks of many computing platforms including laptops, high performance workstations, clusters, grids, and clouds. On the other hand, plenty of hard problems in a wide range of areas including engineering design, telecommunications, logistics and transportation, biology, energy, etc., are often modeled and tackled using optimization approaches. These approaches include greedy algorithms, exact methods (dynamic programming, Branch-and-X, constraint programming, A*, etc.) and meta-heuristics (evolutionary algorithms, particle swarm, ant or bee colonies, simulated annealing, Tabu search, etc.). In many research works, optimization techniques are used to address high performance computing (HPC) issues including HPC hardware design, compiling, scheduling, auto-tuning, etc. On the other hand, optimization problems become increasingly large and complex, forcing the use of parallel computing for their efficient and effective resolution. The design and implementation of parallel optimization methods raise several issues such as load balancing, data locality and placement, fault tolerance, scalability, thread divergence, etc.

This workshop seeks to provide an opportunity for the researchers to present their original contributions on the joint use of advanced (discrete or continuous, single or multi-objective, static or dynamic, deterministic or stochastic, hybrid) optimization methods and distributed and/or parallel multi/many-core computing, and any related issues.

The POMCO Workshop topics include (but are not limited to) the following:

  • Parallel models (island, master-worker, multi-start, etc.) for optimization methods revisited for multi-core and/or many-core (MMC) environments. 
  • Parallelization techniques and advanced data structures for exact (e.g. tree-based) optimization methods.
  • Parallel mechanisms for hybridization of optimization algorithms on MMC environments
  • Parallel strategies for handling uncertainty, robustness and dynamic nature of optimization methods.
  • Implementation issues of parallel optimization methods on MMC workstations, MMC clusters, MMC grids/clouds, etc.
  • Software frameworks for the design and implementation of parallel and/or distributed MMC optimization algorithms
  • Computational/theoretical studies reporting results on solving challenging problems using MMC computing
  • Energy-aware optimization for/with MMC parallel and/or distributed optimization methods
  • Optimization techniques for efficient compiling, scheduling, etc. for MMC environments
  • Optimization techniques for scheduling, compiling, auto-tuning for MMC clusters, MMC grids/clouds, etc.

Coming events

  • Summer School
    27. 08. 2018 - 31. 08. 2018

    SYNERGY Summer School on Efficient Multi-Objective OptimisationLjubl

  • HPOI 2018
    08. 10. 2018

    International Conference on High-Performance Optimisation in Industry

  • META 2018
    27. 10. 2018 - 31. 10. 2018

    7th International Conference on Metaheuristics and Nature Inspired Computing