Video: 6 minute introduction to Frevo
Demonstrated in the video below, 6 minutes are sufficient to download Frevo, install it, set up a simulation, evolve a neural network controller for an inverted pendulum problem and check the results!Video: Complexity on the workbench
István Fehérvári explains complex systems and discusses how to use them in technical engineering. He also explains our tool following this approach, Frevo.Video: Why self-organization?
A short animation about the problems of complex systems and why we should look for a new paradigm in system design.Video: A Self-organizing Robot Soccer Team
In a case study, we have evolved the control behavior for simulated soccer robots to cooperatively win soccer games. After a few hundred generations, the players adopt useful behavior able to defend, attack and score a goal. Note that the players were not evolved to a priori defined roles, like defender, midfielder or striker, but all have an instance of the same neural network controller. Still, during a game, different behavior of the players emerge based on their situations. Thus, similar to biological systems, the entities specify to different roles in a self-organizing way. Since the entities are identical, the system has a high robustness against failure of come of the entities.
Video: How to design Self-Organizing Systems
How to design self-organizing systems? The answer is around us in nature, in other words - Evolution! Evolutionary methods have evolved a big deal of self-organizing systems around us - these are working well and in a robust way in spite of noise and changing conditions. In the DEMESOS project we apply the evolutionary approach to the design of complex technical systems. The video gives a short introduction to the topic and displays some application scenarios.
FREVO: Framework for Evolutionary Design
FREVO is an open-source framework developed in Java to help engineers and scientists in evolutionary design or optimization tasks. The major feature of FREVO is the componentwise decomposition and separation of the key building blocks for each optimization tasks. We identify these as the problem definition, solution representation and the optimization method. This structure enables the components to be designed separately allowing the user to easily swap and evaluate different configurations and methods or to connect an external simulation tool.
