Alexander V. Boukhanovsky

Nikolay O. Nikitin, Pavel Vychuzhanin, Mikhail Sarafanov et al.

The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to computation workflows that consist of models and data operations. The approach combines key ideas of both automated machine learning and workflow management systems. It designs the pipelines with a customizable graph-based structure, analyzes the obtained results, and reproduces them. The evolutionary approach is used for the flexible identification of pipeline structure. The additional algorithms for sensitivity analysis, atomization, and hyperparameter tuning are implemented to improve the effectiveness of the approach. Also, the software implementation on this approach is presented as an open-source framework. The set of experiments is conducted for the different datasets and tasks (classification, regression, time series forecasting). The obtained results confirm the correctness and effectiveness of the proposed approach in the comparison with the state-of-the-art competitors and baseline solutions.

Nikolay O. Nikitin, Iana S. Polonskaia, Anna V. Kalyuzhnaya et al.

In engineering practice, it is often necessary to increase the effectiveness of existing protective constructions for ports and coasts (i. e. breakwaters) by extending their configuration, because existing configurations don't provide the appropriate environmental conditions. That extension task can be considered as an optimisation problem. In the paper, the multi-objective evolutionary approach for the breakwaters optimisation is proposed. Also, a greedy heuristic is implemented and included to algorithm, that allows achieving the appropriate solution faster. The task of the identification of the attached breakwaters optimal variant that provides the safe ship parking and manoeuvring in large Black Sea Port of Sochi has been used as a case study. The results of the experiments demonstrated the possibility to apply the proposed multi-objective evolutionary approach in real-world engineering problems. It allows identifying the Pareto-optimal set of the possible configuration, which can be analysed by decision makers and used for final construction

Pavel A. Smirnov, Sergey V. Kovalchuk, Alexander V. Boukhanovsky

The existing approaches for scientific workflows composition face the problems of domain knowledge integration. By this paper we summarize the results, which have been elaborated and implemented during the 2-year research concerning to Virtual Simulation Objects (VSO) concept and technology development. The contribution of this paper consists of formal models of the VSO internal structures and user-assistance logic, which may be obtained as a result of the reasoning over knowledge base. (This paper was rejected to appear in "Recent Advances in Knowledge Based Technologies and Applications" by Hindawi in 2014, but was stolen and published by Ke Han under the name "The Study on Workflows Composition Based on Virtual Simulation Objects Technology")

Sergey V. Kovalchuk, Pavel A. Smirnov, Konstantin V. Knyazkov et al.

Presented paper describes the development of comprehensive approach for knowledge processing within e-Sceince tasks. Considering the task solving within a simulation-driven approach a set of knowledge-based procedures for task definition and composite application processing can be identified. This procedures could be supported by the use of domain-specific knowledge being formalized and used for automation purpose. Within this work the developed conceptual and technological knowledge-based toolbox for complex multidisciplinary task solv-ing support is proposed. Using CLAVIRE cloud computing environment as a core platform a set of interconnected expressive technologies were developed.