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. arXiv preprint arXiv:2204.03400 (CEC-2022 conference, unconditionally accepted, in press) [2] Grigorev G. V. et al. Single Red Blood Cell Hydrodynamic Traps via the Generative Design //Micromachines. – 2022. – Т. 13. – №. 3. – С. 367. [3] Nikitin N. O. et al. Generative design of microfluidic channel geometry using evolutionary approach //Proceedings of the Genetic and Evolutionary Computation Conference Companion. – 2021. – С. 59-60. 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: +7 906 243 4402 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Nikita Starodubcev email: nstarodubtcev@itmo.ru phone: +7 964 366 0094 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Georgii Grigorev email: georgii@berkeley.edu phone:+15103886410 Data Science and Information Technology Research Center, Tsinghua Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, Mechanical Department, University of California in Berkeley, Berkeley, CA 94703, USA; Alexander Hvatov email: alex_hvatov@itmo.ru phone: +7 952 220 3276 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Alexander Lebedev email: mr.aleksanderl@yandex.ru phone: +79105201504 Machine Building Department, Bauman Moscow State Technical University, 105005 Moscow, Russia Xiaohao Wang email:: wang.xiaohao@sz.tsinghua.edu.cn phone: +86-136-001-864-22 Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China Xiang Qian email: ; qian.xiang@sz.tsinghua.edu.cn phone:+86-755-2603-67-55 Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China Liwei Lin email: lwlin@berkeley.edu phone: +15103886410 Mechanical Department, University of California in Berkeley, Berkeley, CA 94703, USA Georgii Maksimov email: gmaksimov@mail.ru phone: +79032555799 Biophysics Lab, Biology Department, Moscow State University, 119192 Moscow, Russia Physical metallurgy Department, Federal State Autonomous Educational Institution of Higher Education, National Research Technological University, MISiS, 119049 Moscow, Russia Anna Kalyuzhnaya email: anna.kalyuzhnaya@itmo.ru phone: +7 911 038 2768 ITMO University 49 Kronverksky Pr. St. Petersburg 197101 Russian Federation Iana Polonskaia email: ispolonskaia@itmo.ru phone: +7 913 524 7595 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] This paper describes a generative design methodology for a micro hydrodynamic singleRBC (red blood cell) trap for applications in microfluidics-based single-cell analysis. One key challenge in single-cell microfluidic traps is to achieve desired through-slit flowrates to trap cells under implicit constraints. In this work, the cell-trapping design with validation from experimental data has been developed by the generative design methodology with an evolutionary algorithm. L-shaped trapping slits have been generated iteratively for the optimal geometries to trap living-cells suspended in flow channels. Without using the generative design, the slits have low flow velocities incapable of trapping single cells. After a search with 30,000 solutions, the optimized geometry was found to increase the through-slit velocities by 49%. Fabricated and experimentally tested prototypes have achieved 4 out of 4 trapping efficiency of RBCs. This evolutionary algorithm and trapping design can be applied to cells of various sizes. [3] In this paper, we propose the evolutionary approach for the generative design of microfluidic channel geometry. Sets of candidate solutions for geometry of single cell analysis devices can be used to simplify the decision-making process for micro-devices design. The algorithmic core is based on continuous optimization of coordinates of a polygons set. The proposed approach is validated experimentally with the fabricated microfluidic device. The experiments confirm the correctness and effectiveness of the proposed methods. 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/ITMO-NSS-team/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. (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. 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] NO Starodubcev, NO Nikitin, AV Kalyuzhnaya. Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks. arXiv preprint arXiv:2204.03400 (CEC-2022 conference, unconditionally accepted, in press) [2] Grigorev G. V. et al. Single Red Blood Cell Hydrodynamic Traps via the Generative Design //Micromachines. – 2022. – Т. 13. – №. 3. – С. 367. [3] Nikitin N. O. et al. Generative design of microfluidic channel geometry using evolutionary approach //Proceedings of the Genetic and Evolutionary Computation Conference Companion. – 2021. – С. 59-60. 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 GEFST 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] Submitted on 7 Apr 2022 [2] 26 February 2022 [3] 08 July 2021