Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A Dynamic Data Driven Wildland Fire Model
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Parallel Dynamic Data Driven Genetic Algorithm for Forest Fire Prediction
Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
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This work describes a Dynamic Data Driven Genetic Algorithm (DDDGA) for improving wildfires evolution prediction. We propose an universal computational steering strategy to automatically adjust certain input data values of forest fire simulators, which works independently on the underlying propagation model. This method has been implemented in a parallel fashion and the experiments performed demonstrated its ability to overcome the input data uncertainty and to reduce the execution time of the whole prediction process.