The trend toward pervasive computing and networked
systems has led to increased complexity and dynamics of today’s technical
systems. Thus, future systems are expected to be even more complex requiring
novel ways to handle such complex networked systems. One approach to solve this
problem is to increase the level of self-organization in those systems.
Self-organizing systems offer numerous advantages over traditional ones like
robustness against a failure of a component and scalability but due to the
distributed structure there is no straightforward way to design a
self-organizing system.
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.
István Fehérvári, On Evolving Self-organizing Technical Systems, PhD Dissertation, Alpen-Adria-Universität Klagenfurt, 2013. (fulltext-pdf)
