Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Collectives and Design Complex Systems
Collectives and Design Complex Systems
Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A cooperative multi-agent approach to free flight
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Autonomous agents for air-traffic deconfliction
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Distributed agent-based air traffic flow management
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Ant colony optimization for resource-constrained project scheduling
IEEE Transactions on Evolutionary Computation
Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective
The Journal of Machine Learning Research
Adaptive navigation for autonomous robots
Robotics and Autonomous Systems
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Air traffic management offers an intriguing real world challenge to designing large scale distributed systems using evolutionary computation. The ability to evolve effective air traffic flow strategies depends not only on evolving good local strategies, but also on ensuring that those local strategies result in good global solutions. While traditional, direct evolutionary strategies can be highly effective in certain combinatorial domains, they are not well-suited to complex air traffic flow problems because of the large interdependencies among the local subsystems. In this paper, we propose an evolutionary agent-based solution to the air traffic flow problem. In this approach, we evolve agents both to learn the right local flow strategies to alleviate congestion in their immediate surroundings, and to prevent the creation of congestion "downstream" from their local areas. The agent-based approach leads to better and more fault-tolerant solutions. To validate this approach, we use FACET, an air traffic simulator developed at NASA and used extensively by the FAA and industry. On a scenario composed of three hundred aircraft and two points of congestion, our results show that an agent based evolutionary computation method, where each agent uses the system evaluation function, achieves 40% improvement over a direct evolutionary algorithm. In addition by creating agent-specific "difference evaluation functions" we achieve an additional 30% improvement over agents using the system evaluation.