## 2 Background  
The authors of [8] examine the trade-off between per-instance Algorithm selection and running multiple Algorithms in parallel. While Algorithm selection leverages complementary performance among Algorithms, it requires complex models and additional computation. In contrast, parallel execution is feasible on modern hardware but can lead to resource contention and slowdowns. The results suggest that Algorithm selection is beneficial, particularly for large Algorithm portfolios. The work [15] proposed an approach for decreasing the time needed for Algorithm selection training. Usually, the training process includes the evaluation of the performance of all pretrained Algorithms on the testing set. This process can take a lot of time. The authors propose to start by evaluating the small batches of testing data and then gradually increase the size of the evaluation data. During the process, Algorithms can be excluded from the pool based on the cross-validation between their learning curves. This allows for the early exclusion of under-performing Algorithms and can decrease the required training time by 20% to 50% without a noticeable downgrade in terms of accuracy. From the current literature, it is seen that Algorithm selection can help in marginally improving the classification of many problems. Most applications achieve from 1%-5% improvement in relative classification accuracy [16]. Among many studies, the best results for simple Algorithm selection classifiers are often shown by Random Forest, and Support Vector Machines (SVM) classifiers. Boosted ensemble classifiers created with Adaboost also show good results and outperform single classifiers in some cases. Authors of [17] trained a range of content-similarity-assessing Algorithms and used a Random Forest classifier to chose the best one for a particular document instance. The Random Forest classifier had 15% better accuracy than the random choice strategy for Algorithm selection. However, Random Forest-based Algorithm selection still had lower accuracy compared to the best of single Algorithms (0.51 vs. 0.58). The authors of work [13] applied Algorithm selection for segmentation problems on Microsoft Common Objects in Context (MSCOCO) and Visual Object Classes Challenge 2012 (VOC2012) datasets without prior extraction of meta-features. For the VOC2012 dataset, the SVM Algorithm selector showed the best accuracy, while for the MSCOCO dataset, the AdaBoost ensemble showed the best result. The best overall result was achieved using an ensemble of five predetermined Algorithms resulting in 3% higher accuracy than the best single Algorithm. In another work, authors of [10] applied a per-object Algorithm selector by dividing the image into parts and using Algorithm selection for choosing the segmentation Algorithm for each part. This approach resulted in 2% improvement in accuracy using combinations of Artificial Neural Network (ANN) + Preference Rules and ANN + SVM classifiers. A lot of work went into creating fully automated ML learning frameworks that can be unified under the AutoML term. The goal of AutoML systems is to provide users with a fully working and automated pipeline for analyzing the data, choosing the machine learning approach, configuring the Algorithm, and evaluating the results. SmartML [18] and AutoSklearn [19] are examples of such automated suites. SmartML, being the simpler one, executes an extensive grid search on the range of pretrained Algorithms as well as a range of Algorithm selection classifiers. It also experiments with different training, validation, and test splits in the process. The AutoSklearn framework was created for the AutoML competition with the strict requirement of giving the best predictions in under 20 min. The authors achieved the best result by cutting out unpromising combinations for a grid search. They employed a resource-management strategy that devotes processing time and power to the most promising combinations based on pre-collected data.