A framework and analysis for cooperative search using UAV swarms
Proceedings of the 2004 ACM symposium on Applied computing
Evolving Self-Organizing Behaviors for a Swarm-Bot
Autonomous Robots
Evolving cooperative strategies for UAV teams
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Performance of digital pheromones for swarming vehicle control
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Comprehensive Overview of the Applications of Artificial Life
Artificial Life
Genetic team composition and level of selection in the evolution of cooperation
IEEE Transactions on Evolutionary Computation
Using genetic algorithms to evolve the control rules of a swarm of UAVs
CTS'05 Proceedings of the 2005 international conference on Collaborative technologies and systems
Digital pheromones for coordination of unmanned vehicles
E4MAS'04 Proceedings of the First international conference on Environments for Multi-Agent Systems
Towards dependable swarms and a new discipline of swarm engineering
SAB'04 Proceedings of the 2004 international conference on Swarm Robotics
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It is generally challenging to design decentralized controllers for swarms of robots because there is often no obvious relation between the individual robot behaviors and the final behavior of the swarm. As a solution, we use artificial evolution to automatically discover neural controllers for swarming robots. Artificial evolution has the potential to find simple and efficient strategies which might otherwise have been overlooked by a human designer. However, evolved controllers are often unadapted when used in scenarios that differ even slightly from those encountered during the evolutionary process. By reverse-engineering evolved controllers we aim towards handdesigned controllers which capture the simplicity and efficiency of evolved neural controllers while being easy to optimize for a variety of scenarios.