1. the complete title of one (or more) paper(s) published in the open literature describing the work that the author claims describes a human-competitive result; The series of the papers, including: [1] Polonskaia, I. S., Nikitin, N. O., Revin, I., Vychuzhanin, P., & Kalyuzhnaya, A. V. (2021). Multi-Objective Evolutionary Design of Composite Data-Driven Models. arXiv preprint arXiv:2103.01301 (CEC-2021 conference, unconditionally accepted, in press) [2] Nikitin, N. O., Polonskaia, I. S., Vychuzhanin, P., Barabanova, I. V., & Kalyuzhnaya, A. V. (2020). Structural Evolutionary Learning for Composite Classification Models. Procedia Computer Science, 178, 414-423. [3] Kalyuzhnaya, A. V., Nikitin, N. O., Vychuzhanin, P., Hvatov, A., & Boukhanovsky, A. (2020, July). Automatic evolutionary learning of composite models with knowledge enrichment. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 43-44). 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Nikolay Nikitin email: nnikitin@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Pavel Vychuzhanin email: pavel.vychuzhanin@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Irina Barabanova email: ivbarabanova@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Revin Ilia email: ierevin@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Alexander Hvatov email: alex_hvatov@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Alexander Boukhanovsky email: avbukhanovskii@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Anna Kalyuzhnaya email: anna.kalyuzhnaya@itmo.ru phone: ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation 3. the name of the corresponding author (i.e., the author to whom notices will be sent concerning the competition); Nikolay Nikitin 4. the abstract of the paper(s); [1] In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogeneous pipelines that consist of different blocks: machine learning models, data preprocessing blocks, etc. The implemented approach is based on a parameter-free genetic algorithm (GA) for model design called GPComp@Free. It is developed to be part of automated machine learning solutions and to increase the efficiency of the modeling pipeline automation. A set of experiments was conducted to verify the correctness and efficiency of the proposed approach and substantiate the selected solutions. The experimental results confirm that a multi-objective approach to the model design allows achieving better diversity and quality of obtained models. The implemented approach is available as a part of the open-source AutoML framework FEDOT. [2] In this paper, we propose an evolutionary learning approach for flexible identification of custom composite models for classification problems. To solve this problem in an efficient way, the problem-specific evolutionary operators are proposed and the effectiveness of different modifications of the common genetic programming algorithm is investigated. Also, several implementations of caching for the fitted models were compared from the performance point of view. To verify the proposed algorithm, both synthetic and real-world classification cases are examined. The implemented solution can identify the structure of the composite models from scratch, as well as be used as a part of automated machine learning solutions. [3]This paper provides the main concepts of the knowledge-enriched AutoML approach and shortly describes the current results of the proof of concept implementation within the FEDOT framework. By knowledge enrichment, we mean the insertion of domain-specific models and expert-like meta-heuristics. Also, we involve multi-scale learning as a part of complex models identification. The proposed concepts make it possible to create effective and interpretable composite models. 5. a list containing one or more of the eight letters (A, B, C, D, E, F, G, or H) that correspond to the criteria (see above) that the author claims that the work satisfies; (B) The result is equal to or better than a result that was accepted as a new scientific result at the time when it was published in a peer-reviewed scientific journal. (F) The result is equal to or better than a result that was considered an achievement in its field at the time it was first discovered. (G) The result solves a problem of indisputable difficulty in its field. 6. a statement stating why the result satisfies the criteria that the contestant claims (see examples of statements of human-competitiveness as a guide to aid in constructing this part of the submission); The proposed approach to the model design makes it possible to identify the high variety of the modeling pipelines for different tasks. The approach is aimed at the creation of complex, graph-based pipelines with a high variety of building blocks. It makes it significantly different from existing AutoML and workflow management approaches and allows obtaining human-competitive results in different fields. The software implementation of the proposed approach is available in the FEDOT framework (https://github.com/nccr-itmo/FEDOT). (B) The AutoML-based solutions that are described in the literature are focused mostly on model selection and hyperparameter tuning of relatively simple pipelines. Despite the claimed efficiency of obtained solutions for benchmarks, the human-produced solutions for modeling problems usually have a more complex and heterogeneous nature (is consists of several types of building blocks - data sources, data flow control operations, data preprocessing operations, data-driven and domain-specific models, etc). The proposed approach allows creating the pipelines with these properties using an evolutionary approach based on genetic programming. It allows us to obtain human-competitive results in the different fields, which is a more universal and practical approach than existing ones. (F) The state-of-the-art AutoML results in various fields still receive a lot of criticism from the experts in domain fields. Despite the high efficiency of AutoML in several cases (for example, Neural architecture search for image classification tasks), the internal structure of the obtained pipelines is conceptually different from the human-derived results. The proposed approach makes it possible to identify the pipelines for the different tasks, data types, and building blocks. Also, we use the multi-objective problem statement to satisfy all proposed requirements. So, the obtained result can be considered superior to existing ones. (G) The automated pipeline design is considered a complex and difficult problem from different points of view: computational, algorithmic, software implementation. There is not any solution that allows solving it in full scale, so a lot of simplifications and heuristics are applied here. The proposed approach can be used to design the pipelines in a human-like way in a flexible and controllable way. 7. a full citation of the paper (that is, author names; title, publication date; name of journal, conference, or book in which article appeared; name of editors, if applicable, of the journal or edited book; publisher name; publisher city; page numbers, if applicable); [1] Polonskaia, I. S., Nikitin, N. O., Revin, I., Vychuzhanin, P., & Kalyuzhnaya, A. V. (2021). Multi-Objective Evolutionary Design of Composite Data-Driven Models. arXiv preprint arXiv:2103.01301 (CEC-2021 conference, unconditionally accepted, in press) [2] Nikitin, N. O., Polonskaia, I. S., Vychuzhanin, P., Barabanova, I. V., & Kalyuzhnaya, A. V. (2020). Structural Evolutionary Learning for Composite Classification Models. Procedia Computer Science, 178, 414-423. [3] Kalyuzhnaya, A. V., Nikitin, N. O., Vychuzhanin, P., Hvatov, A., & Boukhanovsky, A. (2020, July). Automatic evolutionary learning of composite models with knowledge enrichment. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion (pp. 43-44). 8. a statement either that "any prize money, if any, is to be divided equally among the co-authors" OR a specific percentage breakdown as to how the prize money, if any, is to be divided among the co-authors; Any prize money, if any, is to be divided equally among all co-authors. 9. a statement stating why the authors expect that their entry would be the "best" The proposed approach and its implementation in the open-source FEDOT framework allow achieving a better state of all AutoML industries. The transition from the simple auto-generated pipelines to the complex pipelines with different types of models, tasks, and data sources makes it possible to automate multi-modal tasks with different data types involved in the modeling. The approach is not restricted by human-competitive results in a single domain but allows obtaining the same results for different tasks in an automated way. For our opinion. it makes it a quite promising candidate for Humies competition. 10. An indication of the general type of genetic or evolutionary computation used, such as GA (genetic algorithms), GP (genetic programming), ES (evolution strategies), EP (evolutionary programming), LCS (learning classifier systems), GI (genetic improvement), GE (grammatical evolution), GEP (gene expression programming), DE (differential evolution), etc. GP 11. The date of publication of each paper. If the date of publication is not on or before the deadline for submission, but instead, the paper has been unconditionally accepted for publication and is "in press" by the deadline for this competition, the entry must include a copy of the documentation establishing that the paper meets the "in press" requirement. [1] Conference date: June 29, 2021 - July 1, 2021 [2] 2020, September [3] 2020, July