Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Automatic aircraft conflict resolution using genetic algorithms
SAC '96 Proceedings of the 1996 ACM symposium on Applied Computing
A Genetic Algorithm to Improve an Othello Program
AE '95 Selected Papers from the European conference on Artificial Evolution
Cooperative Coevolution for Learning Fuzzy Rule-Based Systems
Selected Papers from the 5th European Conference on Artificial Evolution
Proceedings of the 35th conference on Winter simulation: driving innovation
Methods for Operations Planning in Airport Decision Support Systems
Applied Intelligence
Stochastic training of a biologically plausible spino-neuromuscular system model
Proceedings of the 9th annual conference on Genetic and evolutionary computation
ForMAAD: A formal method for agent-based application design
Web Intelligence and Agent Systems
Simultaneous optimization of artificial neural networks for financial forecasting
Applied Intelligence
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As air traffic keeps increasing, many research programs focus on collision avoidance techniques. For short or medium term avoidance, new headings have to be computed almost on the spot, and feed forward neural nets are susceptible to find solutions in a much shorter amount of time than classical avoidance algorithms (iA*, stochastic optimization, etc.) In this article, we show that a neural network can be built with unsupervised learning to compute nearly optimal trajectories to solve two aircraft conflicts with the highest reliability, while computing headings in a few milliseconds.