# Data-Driven Computational Methods in Fuel Combustion: A Review of Applications
## 3 Analysis of the State of the Art
### Methods and Algorithms
#### 3.1.5 Genetic Algorithms as a Universal Optimization Tool  
Genetic algorithms appear as a key component of many optimization systems. In Ref. [46], they were used to adjust the kinetic parameters of combustion of large biomass samples. In Ref. [9], as well as in Refs. [10; 11; 12; 47; 48], GA supported the optimization of injection systems, piston geometry, and GCI engine operation. In Ref. [49], GA was used to simplify the kinetic mechanism of methane reforming, achieving a reduction in computation time without compromising accuracy. Although it is often assumed in the literature that genetic algorithms are able to effectively explore the multidimensional space of fuel combinations, there is a lack of clear analyses confirming the effectiveness of these methods in the context of different types of fuels and combustion conditions. In many cases, it is not clearly defined how the algorithm's ability to find global optimums in complex parametric spaces was evaluated. For example, work on the optimization of biofuel and synthetic fuel blends rarely indicates how the algorithm coped with the traps of local minima or how the stability of solutions was tested when input conditions (e.g., pressure, temperature, air-fuel ratio) were tested. To increase the reliability of the study, the authors should consider introducing comparative tests on established benchmarks (e.g., sets of known fuel configurations with assessed combustion efficiency), as well as using techniques such as cross-validation, sensitivity analysis, and testing the algorithm on simulated data with known properties. A lack of such validation can lead to overinterpretation of the results and the adoption of false assumptions about the universality and effectiveness of the algorithm, especially in industrial applications where operating parameters are constantly changing. In Ref. [50], a genetic algorithm enabled the design of an industrial burner with reduced NOx emissions. Refs. [51; 52] prove its usefulness also in the design of fuel composition: in the first one, the operating parameters of the dual-fuel engine were optimized, and in the second, the composition of mixtures with the required octane number was utilized, while minimizing the costs of the components. In Ref. [53], the genetic algorithm and the KIVA-3V code were used to optimize the injection and geometry of the combustion chamber in a diesel/natural gas dual-fuel engine. In Ref. [54], the NSGA-II genetic algorithm was used for multi-criteria optimization of the geometry of the cyclone used in the combustion of pulverized coal, which allowed for an increase in combustion efficiency of 47% and a reduction in NOx emissions of 19%. Response area-based optimization took into account parameters such as cyclone diameter, number, and angle of flow control vanes. In Ref. [55], the NSGA-III algorithm was used for multi-criteria optimization of the DI-NG engine,obtaining compromise operating configurations that meet EURO V emission standards at different combustion modes. In Ref. [56], GPR and NSGA-II were used to model and optimize engine performance on COEE8 biodiesel, effectively predicting emissions and fuel consumption with little data. In Ref. [57], CFD was combined with NSGA to optimize the performance of RCCI engines with gasoline/diesel and methanol/diesel mixtures, taking into account the influence of fuel properties and initial conditions. On the other hand, in Ref. [58], genetic algorithms were used for automatic calibration of combustion diagnostic parameters in a spark engine fueled by natural gas, in order to better understand cyclic variability.