Firefly-inspired sensor network synchronicity with realistic radio effects
Proceedings of the 3rd international conference on Embedded networked sensor systems
Emergent (mis)behavior vs. complex software systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Continuous Space-Time Semantics Allow Adaptive Program Execution
SASO '07 Proceedings of the First International Conference on Self-Adaptive and Self-Organizing Systems
Engineering Self-Adaptive Systems through Feedback Loops
Software Engineering for Self-Adaptive Systems
A generic quantitative relationship between quality of experience and quality of service
IEEE Network: The Magazine of Global Internetworking - Special issue on improving quality of experience for network services
The Internet of Things: A survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
ASH: Tackling Node Mobility in Large-Scale Networks
SASO '10 Proceedings of the 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems
Finding forms of flocking: evolutionary search in ABM parameter-spaces
MABS'10 Proceedings of the 11th international conference on Multi-agent-based simulation
Spatial computing: an emerging paradigm for autonomic computing and communication
WAC'04 Proceedings of the First international IFIP conference on Autonomic Communication
IEEE Transactions on Evolutionary Computation
Complex software project development: agile methods adoption
Journal of Software Maintenance and Evolution: Research and Practice
Event-based graphical monitoring in the EpochX genetic programming framework
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Automatic algorithm generation for large-scale distributed systems is one of the holy grails of artificial intelligence and agent-based modeling. It has direct applicability in future engineered (embedded) systems, such as mesh networks of sensors and actuators where there is a high need to harness their capabilities via algorithms that have good scalability characteristics. NetLogo has been extensively used as a teaching and research tool by computer scientists, for example for exploring distributed algorithms. Inventing such an algorithm usually involves a tedious reasoning process for each individual idea. In this paper, we report preliminary results in our effort to push the boundary of the discovery process even further, by replacing the classical approach with a guided search strategy that makes use of genetic programming targeting the NetLogo simulator. The effort moves from a manual model implementation to an automated discovery process. The only activity that is required is the implementation of primitives and the configuration of the tool-chain. In this paper, we explore the capabilities of our framework by re-inventing five well-known distributed algorithms.