Project EVOSOS successfully concluded!

Within the project EVOSOS, we investigated how evolutionary computation can be applied to find the appropriate local rules that provide the desired emergent global behavior for a given system. In particular, we propose a design methodology built on meta-heuristic search that can guides the designer throughout the whole engineering process. Additionally, we investigate the evolvability of self-organizing technical systems via several case studies focusing on the effects of certain design decisions explained in the proposed methodology. First, a self-organizing cellular automata model is described that is evolved to present a desired 2D structure. Using this example the connection between problem complexity and evolvability is discussed.
Two further studies focus on evolutionary swarm robotics. In the first one, we discuss the effects of various interaction interfaces and their effects on the quality of the evolved solutions. We find that seemingly identical interfaces can produce significantly different group behavior. The second experiment investigates how a self-organizing team of soccer robots can be evolved. Here, we study the effects of different agent controller structures and interface interpretation models.
A further outcome of the project is the evolutionary software framework FREVO, which implements the proposed design methodology and thus aids engineers and researchers working with self-organizing systems.

Publications (with link to fulltexts):

• I. Fehervari and W. Elmenreich. Evolution as a tool to design self-organizing systems. In Self-Organizing Systems, volume LNCS 8221, pages 139–144. Springer Verlag, 2014.
• István Fehérvári, On Evolving Self-organizing Technical Systems, PhD Dissertation, Alpen-Adria-Universität Klagenfurt, 2013.
• I. Fehérvári, V. Trianni, and W. Elmenreich. On the effects of the robot configuration on evolving coordinated motion behaviors. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, June 2013.
• I. Fehérvári, A. Sobe, and W. Elmenreich. Biologically sound neural networks for embedded systems using OpenCL. In Proceedings of the International Conference on NETworked sYStems (NETYS 2013). Springer Verlag, May 2013.
• R. M. W. Masood, J.Klinglmayr, I. Fehervari, W. Watzl, C. Bettstetter: Demo Abstract: Synchronization using Inhibitory and Excitatory Coupling: From Theory to Practice, in: The 32nd IEEE International Conference on Computer Communications, IEEE, Piscataway (NJ), April 2013.
• I. Fehérvári, W. Elmenreich, and E. Yanmaz. Evolving a team of self-organizing UAVs to address spatial coverage problems. In R. M. Bichler, S. Blachfellner, and W. Hofkirchner, editors, European Meeting on Cybernetics and Systems Research Book of Abstracts, pages 201–204, Vienna, Austria, April 2012.
• A. Sobe, I. Fehérvári, and W. Elmenreich. FREVO: A tool for evolving and evaluating self-organizing systems. In Proceedings of the 1st International Workshop on Evaluation for Self-Adaptive and Self-Organizing Systems, Lyon, France, September 2012.
• W. Elmenreich and I. Fehérvári. Evolving self-organizing cellular automata based on neural network genotypes. In Proceedings of the Fifth International Workshop on Self-Organizing Systems, volume LNCS 6557, pages 16–25. Springer Verlag, 2011.
• I. Fehervari, B. Lenart: Using an Adaptive Neuro-Fuzzy Inference System for Adaptive Inventory Control, in: Proceedings of the International Conference on Innovative Technologies (IN-TECH) 2011, Czech Technical University, Faculty of mechanical engineering, Department of manufacturing technology, Prague, 2011, S. 43 - 48.
• O. Maurhart, W. Elmenreich, I. Fehervari, A. Bouchachia: Evaluation of Robustness and Performance of Environmental Influences on Evolutionary Algorithms compared to Ant Colony Systems, in: European Conference on Complex Systems (ECCS'11 Vienna), Löcker Verlag, Wien, 2011, S. 98 - 99.

Complexity on the workbench

Today’s technical systems contain more and more components which are typically networked and interacting with each other. So, these systems become very complex, which makes it difficult to engineer and maintain the system using traditional, hierarchical approaches.
Looking into complex systems in nature, we see that they are controlled by distributed self-organizing mechanisms that are simple, scalable, robust, and adaptive. However, putting a self-organizing approach into technical systems is not straightforward, because such complex systems are typically hard to predict. A particular change in an interaction mechanism might even have counter-intuitive effects.
In nature, the driving mechanism behind building self-organizing behavior is evolution - why not use the very same method in form of an evolutionary algorithm?
However, there is a need to integrate different tools and models like neural networks, mutation and recombination, and problem-specific simulations. With our tool FREVO we provide a unifying framework to reduce this problem to basically three components: a problem representation, an agent representation and an evolutionary algorithm.
FREVO has been used to solve quite different problems and is available as open source to everyone. It is a very flexible framework open to new components and simulations, thus, we are looking forward to see you testing your ideas with it :-)



This talk was originally given by István Fehérvári at FET 2011 in the science café. This work was supported in part by the Lakeside Labs project MESON (Modeling and Engineering of Self-Organizing Networks) and the Lakeside Labs GmbH.