Computation in artificially evolved, non-uniform cellular automata
Theoretical Computer Science - Special issue: cellular automata
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
Evolution of Parallel Cellular Machines: The Cellular Programming Approach
SOS++: finding smart behaviors using learning and evolution
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Evolving Multi-creature Systems for All-to-All Communication
ACRI '08 Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry
Optimal 6-state algorithms for the behavior of several moving creatures
ACRI'06 Proceedings of the 7th international conference on Cellular Automata for Research and Industry
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
All-to-all communication with CA agents by active coloring and acknowledging
ACRI'10 Proceedings of the 9th international conference on Cellular automata for research and industry
| Hi-index | 0.00 |
We have investigated the all-to-all communication problem for a multi-agent system modeled in cellular automata. The agents' task is to solve the problem by communicating their initially mutually exclusive information to all the other agents. In order to evolve the best behavior of agents with a uniform rule we used a set of 20 initial configurations, 10 with border, 10 with cyclic wrap-around. The behavior was evolved by a genetic algorithm for agents with (1) simple moving abilities, (2) for agents with more sophisticated moving abilities and (3) for agents with indirect communication capabilities (reading and writing flags into the environmental cells). The results show that the more sophisticated agents are not only more effective but also more efficient regarding the effort that has to be made finding a feasible behavior with the genetic algorithm.