Evolutionary Statistical System Based on Novelty Search: A Parallel Metaheuristic for Uncertainty Reduction Applied to Wildfire Spread Prediction

The problem of wildfire spread prediction presents a high degree of complexity due in large part to the limitations for providing accurate input parameters in real time (e.g., wind speed, temperature, moisture of the soil, etc.). This uncertainty in the environmental values has led to the development of computational methods that search the space of possible combinations of parameters (also called scenarios) in order to obtain better predictions. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide this search. Moreover, the resulting predictions are based on a combination of multiple solutions from the space of scenarios. These methods have improved the quality of classical predictions; however, they have some limitations, such as premature convergence. In this work, we evaluate a new proposal for the optimization of scenarios that follows the Novelty Search paradigm. Novelty-based algorithms replace the objective function by a measure of the novelty of the solutions, which allows the search to generate solutions that are novel (in their behavior space) with respect to previously evaluated solutions. This approach avoids local optima and maximizes exploration. Our method, Evolutionary Statistical System based on Novelty Search (ESS-NS), outperforms the quality obtained by its competitors in our experiments. Execution times are faster than other methods for almost all cases. Lastly, several lines of future work are provided in order to significantly improve these results.