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
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
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The automated optimization of air traffic flow is a critical component of the next generation air traffic system, designed to facilitate the future expansion of air traffic with little increase in infrastructure. While many traditional optimization approaches have been applied to the air traffic flow problem, they have difficulty scaling to large problems and in handling the nonlinearities inherent in the air traffic flow patterns. As a solution, this paper shows how genetic algorithms can be successfully applied to this problem. With this approach, the airspace is broken up into separate control points, with a single gene within a chromosome controlling an individual point. A genetic algorithm can then be used to find a controller that maximizes the performance of the airspace. 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 one thousand aircraft and two points of congestion, our results show that the evolutionary method provides 60% higher performance than more traditional Monte Carlo methods