|Evolutionary design process|
In particular, the following aspects need to be considered:
- Accuracy/granularity of the simulation?
- Physical capabilities of the agent + environment
- How many agents, homogeneous/heterogeneous configuration?
- How should the agent interact with the environment/other agents
- Number/type of sensors
- Ability to change the environment (enable stygmergy)
- Must be evolvable
- Smooth search space, not too large
- Genotype-to-phenotype mapping
- There exists literally a zoo on metaheuristic optimization algorihtms (Cuckoo search, Honeybee, Frog leap, Firefly, ...)
- Ability to find global optimum
- Number of tweaking parameters?
- Should contribute to a smooth search space
- Avoid modeling the solution instead of the problem
- Mapping of multiple objectives (or weighted sum?)
- Is a way to implement an evolutionary design task for multi-agent system
- Needs a simulation of the problem
- Interface for sensor/actuator connections to the agents
- Feedback from a simulation run -> objective function
- Written in Java, runs on multiple platforms including Linux, Mac OS, Windows
- FREVO is available as open soure at http://frevo.sourceforge.net/
- FREVO introduction video including installation, setting up a simulation and running it (length 6 minutes)
- I. Fehérvári and W. Elmenreich. Evolution as a tool to design self-organizing systems. In Proceedings of the 7th International Workshop on Self-Organizing Systems. Springer Verlag, May 2013.
- 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.