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] Starodubcev N. O., Nikitin N. O., Kalyuzhnaya A. V. Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks //2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 1-8. [2] Starodubcev, N. O., Nikitin, N. O., Andronova, E. A., Gavaza, K. G., Sidorenko, D. O., Kalyuzhnaya, A. V. Generative design of physical objects using modular framework // Engineering Applications of Artificial Intelligence. – 2023. – Т. 119. – С. 105715. 2. the name, complete physical mailing address, e-mail address, and phone number of EACH author of EACH paper(s); Nikita Starodubcev email: nstarodubtcev@itmo.ru phone: +7 964 366 0094 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Nikolay Nikitin email: nnikitin@itmo.ru phone: +7 906 243 4402 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Elizaveta Andronova email: andronova.veta@gmail.com phone: +7 963 327 2001 Peter the Great St. Petersburg Polytechnic University 29 Polytechnic St. St. Petersburg 194064 Russian Federation Konstantin Gavaza, email: gavaza239@gmail.com phone: +79995365098 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Denis Sidorenko email: sidorenko.den95@gmail.com phone: +7 921 6403243 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Anna Kalyuzhnaya email: anna.kalyuzhnaya@itmo.ru phone: +7 911 038 2768 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 the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time. [2] In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth owing to a promising outlook. However, existing approaches are limited by the specificity of the problem under consideration. In addition, they do not provide desired flexibility. In this paper we formulate a general approach to an arbitrary generative design problem and propose a novel framework called GEFEST (Generative Evolution For Encoded STructure) on its basis. The developed approach is based on three general principles: sampling, estimation and optimization. This ensures the freedom of method adjustment for the solution of a particular generative design problem and therefore enables the construction of the most suitable one. A series of experimental studies was conducted to confirm the effectiveness of the GEFEST framework. It involved synthetic and real-world cases (coastal engineering, microfluidics, thermodynamics and oil field planning). Flexible structure of the GEFEST makes it possible to obtain the results that surpassing baseline solutions 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 surrogate-assisted approach to the generative design named GEFEST (Generative Evolution For Encoded STructures) makes it possible to identify the wide variety of the physical objects that interact with continuum media in a frame of different tasks. The approach aims to design complex physical objects that can be described as a set of polygons, line segments or points. It makes it significantly different from existing approaches for topological optimisation and allows obtaining human-competitive results in different fields. The software implementation of the proposed approach is available in the open-source GEFEST framework (https://github.com/aimclub/GEFEST). (B) We used the GEFEST approach to obtain effective configurations for different cases. The first one - the microfluidic cell trap case that considers the automated design of the single red blood cell traps geometry. The simulation of traps with blood flow is reconstructed using the COMSOL simulator. The obtained experimental results confirm that the quality metrics for obtained structures are equal to the expert-design baseline. The second case is a design of attached and detached coastal breakwaters. The usage of multi-objective formulation in GEFEST allows for designing several compromise solutions that can be used for expert-based decision making. The third case is a design of heat-source systems that are part of an electronic microdevice that poses a source of heat. The control over the temperature distribution within the microdevice plays a significant role in practical applications. GEFEST allows you to obtain good initial configurations for making expert decisions. (F) The state-of-the-art results of topology optimization in various fields still receive a lot of criticism. In fact, the structure of the obtained object is derived from human-based solutions. In other words, topology optimization seeks to enhance performance and reduce the weight of the already existing objects. Conversely, in GEFEST there is no prior knowledge about an initial object, it ”generates” structures based on space constraints and design goals only. The proposed GEFEST approach makes it possible to identify the design of physical objects from scratch using the specified simulation-based objectives. So, the obtained results can be considered superior to existing ones. (G) The generative design is considered a complex and difficult problem from different points of view: computational, algorithmic, and software implementation. There is not any solution that allows solving it on the full scale. The GEFEST approach can be used to (a) design the physical objects from scratch; (b) integrate different simulators, constraints, and task-specific objective functions; (c) attach deep surrogate models to reproduce the interaction between continuum media and designed objects in a less computationally expensive 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] Starodubcev N. O., Nikitin N. O., Kalyuzhnaya A. V. Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks //2022 IEEE Congress on Evolutionary Computation (CEC). – IEEE, 2022. – С. 1-8. [2] Starodubcev, N. O., Nikitin, N. O., Andronova, E. A., Gavaza, K. G., Sidorenko, D. O., Kalyuzhnaya, A. V. Generative design of physical objects using modular framework // Engineering Applications of Artificial Intelligence. – 2023. – Т. 119. – С. 105715. 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 GEFEST framework allow solving the tasks of generative design for physical structures of different complexity. 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. Due to the modular implementation of the approach, it can involve various simulations, constraints, evolutionary optimisers and genotypes encodings to adapt the GEFEST to a specific task or field. In 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. GA 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] August 18, 2022 [2] December 4, 2022