# Evolutionary Statistical System Based on Novelty Search:
## 5 Conclusions  
In this work, we have proposed a new parallel metaheuristic approach for uncertainty reduction applied to the problem of wildfire propagation prediction. It is based on previous methods that also use parallel metaheuristics, but in this case, we follow the recently developed _Novelty Search_ paradigm. We designed a novel genetic algorithm based on NS, which guides the evolution of the population according to the novelty of the solutions. During the search, the solutions of highest fitness are stored and then returned as the solution set at the end of the evolution process. The results obtained with this new method show consistent improvements in quality and execution times with respect to previous approaches. While in this work we have experimented with the particular use case of wildfires, the scope of application of ESS-NS can be extended in at least three ways. Firstly, given that the fire simulator is used as a black box, it could potentially be replaced with another one if necessary. Secondly, as with its predecessors, another propagation simulator might also be used in order to adapt the system for the prediction of different phenomena, such as floods, avalanches, or landslides. Thirdly, although ESS-NS was designed for this kind of phenomena, the applications of its internal optimization algorithm (Algorithm 1) are wider; it can potentially be applied to any problem that can be adapted to a GA representation. Regarding the weaknesses and limitations of ESS-NS, these are directly related to the requirements of the system: it depends on a fire simulator (which has its own sources of errors), it needs to obtain maps from the real fire spread at each step, and two simulation steps are needed before a first prediction can be made. In addition, it is possible that many variables are unknown, and this can cause the search process to be more resource-consuming. However, provided that the appropriate resources can be obtained, i.e., hardware and real-time information of the terrain, this method is already capable of producing useful predictions for real-world decision-making tasks. As next steps, the two most interesting paths are related to the parallelization and the behavioral characterization. On the one hand, our current version of ESS-NS only takes advantage of parallelization at the level of the fitness evaluations. Different parallelization techniques could be applied in order to improve quality, execution times, or both. Regarding quality, the most straightforward of these methods would be an island model, such as the ones in ESSIM-EA and ESSIM-DE, but with migration strategies designed specifically for NS. The island model would allow the search process to be carried out with several populations at once, increasing the level of exploration and, as a consequence, the quality of the final results. A migration strategy could even introduce hybridization with a fitness-based approach. Other parallelization approaches could be applied to the remaining sequential steps of Novelty search, e.g., novelty score computation. On the other hand, the current behavioral characterization is based on fitness values, which may bias the search towards high fitness depending on the parameters of the metaheuristic, e.g., the population size. An interesting hypothesis is that a different behavioral characterization based on the simulation results may be a better guide for the exploration of the search space. Therefore, another possibility of improvement could be a novelty score that relies on the distance between simulated maps instead of on the fitness difference. Lastly, another possibility is the design of a dynamic size archive and/or solution set, a novelty threshold for including solutions in the archive as in [19] or even switching the underlying metaheuristic and adapting its mechanisms to the application problem. Conceptualization, J.S., P.C.-S. and G.B. ; methodology, P.C.-S. and G.B. ; software, J.S. ; formal analysis, J.S., P.C.-S. and G.B. ; investigation, J.S. ; resources, P.C.-S. and G.B.