Workshops

Student Workshop on Bioinspired Optimization Methods and their Applications (BIOMA 2014)

Organizers: Jurij Šilc, Aleš Zamuda

Natural phenomena, like the evolution of species, emergent behavior of biological societies, and functioning of the vertebrate immune system, have inspired computer scientists to design advanced problem-solving techniques, known as evolutionary algorithms, ant colony optimization, and artificial immune systems. These and many other bioinspired algorithms that continue to emerge, overcome many limitations of traditional algorithms and have been widely accepted in science, engineering and business. Parallel solving intricate optimization problems in these domains is of particular interest to both designers and practitioners of bioinspired algorithms.
The aim of this workshop is to provide an international forum for doctoral students to discuss a range of topics that are current in research. The workshop provides an excellent opportunity to share provocative ideas, interesting preliminary work, or innovative research directions related to the use of bioinspired optimization techniques and their respective applications. The purpose of the workshop is as well to support the development of networks for young researchers in this area, both with senior researchers and with other graduate students.

Workshop website

Semantic Methods in Genetic Programming

Organizers: Colin Johnson, Krzysztof Krawiec, Alberto Moraglio, Michael O’Neill

Genetic programming (GP)—the application of evolutionary computing techniques to the creation of computer programs—has been a key topic in computational intelligence in the last couple of decades. In the last few years a rising topic in GP has been the use of semantic methods. The aim of this is to provide a way of exploring the input-output behaviour of programs, which is ultimately what matters for problem solving. This contrasts with much previous work in GP, where operators transform the program code and the effect on program behaviour is indirect. This new approach has produced substantially better results on a number of problems, both benchmark problems and real-world applications in areas such as pharmacy; and, has been grounded in a body of theory, which also informs algorithm design. All aspects of research related to Semantic Methods in Genetic Programming will be considered, including both theoretical and empirical work.
There will be a special issue of the journal Genetic Programming and Evolvable Machines on the subject of Semantic Methods in Genetic Programming, and selected authors from the workshop will be encouraged to submit extended versions of their papers for the special issue.

Workshop website

Workshop on Nature-Inspired Techniques for Robotics

Organizers: Claudio Rossi, Nicolas Bredeche, Kasper Stoy

In recent years, there have been a growing number of nature-inspired approaches to robotics, from designing control architecture to robot morphologies, from considering single robot to adaptive collective systems, from bio-inspired decision models to bio-inspired learning algorithms.
The purpose of the workshop on Nature-inspired techniques for robotics is to analyze the state-of-art / state-of-knowledge in this field. The workshop is intended as a melting pot for engineers, researchers and experts working on different disciplines, fostering interdisciplinary debate between fields such as neuro-evolution, evolutionary design, artificial life, evolutionary robotics, development and learning in robotics, adaptive collective robotic systems, etc.

Workshop website

Scaling Behaviours of Landscapes, Parameters and Algorithms

Organizers: Ender Özcan, Andrew J. Parkes

All too often heuristics and meta-heuristics for combinatorial optimisation problems require significant parameter tuning to work most effectively. Often this tuning is performed without any a priori knowledge as to how good values of parameters might depend on features of the problem. This lack of knowledge can lead to lot of computational effort and also has the danger of being limited to only problem instances that are similar to those that have been seen before. The aim of the workshop is to support the development of methods to give deeper insight into problem classes, and how to obtain and exploit structural information. The target participants will be those that:

• work on the theory of search algorithms, but are seeking ways for the theory to have a practical impact;
• work on direct applications, but are frustrated with the trial-and-error approaches that often are often used, and would like to bring 'theoretically-inspired methods' into their work;
• work on flexible frameworks supporting interchangeability and reusability of components and a closer integration between parameter selection and the algorithm.

Workshop website

Advances in Multimodal Optimization

Organizers: Mike Preuss, Michael G. Epitropakis, Xiaodong Li

The workshop attempts to bring together researchers from evolutionary computation and related areas who are interested in Multimodal Optimization. This is a currently forming field, and we aim for a highly interactive and productive meeting that makes a step forward towards defining it. The Workshop will provide a unique opportunity to review the advances in the current state-of-the-art in the field of Niching methods. Further discussion will deal with several experimental/theoretical scenarios, performance measures, real-world and benchmark problem sets and outline the possible future developments in this area. Positional statements, suggestions, and comments are very welcome!

Workshop website

In Search of Synergies between Reinforcement Learning and Evolutionary Computation

Organizers: Madalina M. Drugan, Bernard Manderick

A recent trend in machine learning is the transfer of knowledge from one area to another. In this workshop, we focus on potential synergies between reinforcement learning and evolutionary computation: reinforcement learning (RL) addresses sequential decision problems in an initially unknown stochastic environment, requiring lots of computational resources while the main strength of evolutionary computation (EC) is its general applicability and computational efficiency. Although at first they seem very different, these two learning techniques address basically the same problem: the maximization of the agent's reward in a potentially unknown environment that is not always completely observable. Possibly, these machine learning methods can benefit from an exchange of ideas resulting in a better theoretical understanding and/or empirical efficiency. There are already few examples that exploit the potential synergy between EC and RL. One example is multi-objective reinforcement learning. This is a variant of reinforcement learning that uses multiple rewards instead of a single one. Techniques from multi-objective EC are used for multi-objective RL in order to improve the exploration-exploitation tradeoff. An example in the other direction is the problem of selecting the best genetic operator that is similar to the problem of an RL-agent has to choose between alternatives while maximizing its cumulative expected reward.
The main goal of this workshop is to solicit research and to start the discussion on potential synergies between RL and EC. We want to bring together researchers from machine learning, optimization, and artificial intelligence interested in searching difficult environments that are moreover possibly dynamic, uncertain and partially observable. We also encourage submissions describing applications of EC and RL for games, neural networks, and other real-world applications. Ideally, this workshop will help researchers with a background in either RL or EC to find synergies between their work as well as new challenges and ideas.

Workshop website

Natural Computing for Protein Structure Prediction

Organizers: José Santos Reyes, Gregorio Toscano, Julia Handl

Independent of its starting conformation, a protein in its natural environment folds into a unique three dimensional structure, the native structure. Understanding the native structure of a protein is crucial, as the structure can provide insight into the functional roles of a protein and the specific mechanisms of its biological function. As the output of experimentally determined protein structures lags behind the output of protein sequences, the computational prediction of protein structure remains a “holy grail” of computational biology.
The aim of this workshop is to provide a forum for the exchange and communication of ideas, proposals and results related to the use of nature-inspired techniques in problems related to computational protein structure prediction. In tackling this important problem, nature-inspired techniques are currently being used in a variety of ways, but presentations related to this work are often distributed across a range of sessions / conferences / journals dependent on the particular sub-problem considered / algorithm used. It is hoped that this workshop will act as a meeting point for those authors and attendants of the PPSN conference who have a current or developing interest in this area.

Workshop website

Renewable Energy and Evolutionary Computation - CANCELLED

Organizers: Paul Kaufmann, Oliver Kramer, Frank Neumann, Markus Wagner

Sustainability is of great importance due to increasing demands and limited resources world-wide. In particular, in the field of energy production and consumption methods are required that allow to produce renewable energy in an efficient way as well as develop methods for the efficient usage of energy. The vast extension of renewable energy sources, and the growing information structure allow a fine screening of energy resources, but also require the development of tools for the analysis and understanding of huge datasets about the energy grid.
The goal of this workshop is to provide a forum for researchers using as well as working on evolutionary computing methods for sustainable development. Topics include (but are not limited to): Wind, solar and other renewable energy sources; Planing and control in smart grid; Balancing strategies for integration of volatile sources; Network restoration; Smart metering; Forecasting of energy production and consumption; Energy markets and economic aspects; Hybrid electric and other plug-in vehicles; Real-time and hardware-in-the-loop smart grid simulation; Resource visualization and monitoring.