# Evolutionary Statistical System Based on Novelty Search:
## 2 Related Works
### ESSIM-EA and ESSIM-DE Frameworks
In this new scheme, the **SS** is carried out by the Master, the **CS** is performed by both the Master and the Monitor, and the **PS** is handled only by the Monitor. The **CS** starts with the **CS-Master** block, where each Master generates a probability matrix and provides its \(K_{sign}\) value. The block **CS-Monitor** represents the final part of the calibration in which the Monitor receives all matrices sent by the Masters and then selects the best candidate based on their fitness (these fitness values have already been computed by the Masters). The matrix selected by the Monitor is used for producing the current step prediction. Experimentally, when ESSIM-EA was introduced, it was shown to be able to reach predictions of similar or higher quality than ESS but with a higher cost in execution times. Later, ESSIM-DE was developed, and it significantly reduced response times, but the quality did not improve in general. Subsequent works have improved the performance of the ESSIM-DE method by embedding tuning strategies into the process [23]. Tuning strategies are methods for improving the performance of an application by calibrating critical aspects. Application tuning can be automatic (when the techniques are transparently incorporated in the application) and/or dynamic (adjustments occur during execution) [24]. For ESSIM-DE, there are two automatic and dynamic tuning metrics which have been shown to mitigate the issues of premature convergence and population stagnation present in the case of application of the algorithm. One metric is a population restart operator [13], and the other involves the analysis of the IQR factor of the population throughout generations [25]. The results showed that ESSIM-DE enhanced with these metrics achieved better quality and response times with respect to the same method without tuning. Even though the two approaches described provide improvements over ESS, they are still limited in several fundamental aspects. The first aspect has to do with the design of the metaheuristics that were adapted for use in the **OS** of the ESSIM systems. These are population-based algorithms that are traditionally used with the objective of selecting a single solution: the best individual from the final population, obtained from the last iteration of the evolution process. Instead, the adaptations implemented use the final population to select a set of solutions for the **CS** and **PS**. Since evolutionary metaheuristics tend to converge to a population of similar genotypes, that is, of individuals which are similar in their representation, the population evolved for each prediction step may consist of a set of scenarios that are similar to each other and therefore produce similar simulations. This may be a limiting factor for the uncertainty reduction objective. To understand this, it must be noted that complex problems do not usually have a smooth fitness landscape, which may imply that individuals that are genotypically far apart in the search space may still have acceptable fitness values and could be valuable solutions. Thus, the metaheuristics implemented in the ESSIM systems may leave out these promising candidates. The second aspect is that in the case of ESSIM-DE, the baseline version performs worse than ESS and ESSIM-EA with respect to quality, which led to the design of the previously mentioned tuning mechanisms. These variants produced better results in quality than the original version of ESSIM-DE but did not significantly outperform ESSIM-EA. Such findings seem to support the idea that this particular application problem could benefit from a greater exploration power, combined with a strategy that could take better advantage of the solutions found during the search. From this reasoning, we arrived at the idea of applying a paradigm that maximizes exploration, while keeping solutions in a way that is compatible by design with a DDM-MOS, that is, a system that is based on multiple overlapped solutions.