Vědeckým cílem projektu je navrhnout pokročilé evoluční algoritmy (EA), které budou použitelné v současných komplexních inženýrských optimalizačních a návrhových úlohách. Dalším cílem je tyto algoritmy adaptovat pro různé cílové platformy, ať už pro výkonné GPU (Graphic Processing Unit) a superpočítače nebo naopak pro nízkopříkonové vestavěné systémy. Projekt je rozdělen do tří etap řešení, resp. do tří fází řešení - tzv. pracovních balíčků (WP1 - 3). V první fázi řešení budou navrhovány nové a hybridní evoluční algoritmy, včetně jejich formálního popisu. Ve druhé fázi budou realizovány implementace HPC (High Performance Computing) a vestavěných systémů s důrazem na definovano efektivitu (výpočetní výkon, škálovatelnost, energetickou náročnost algoritmu). V třetí fázi budou řešeny praktické aplikace, v projektu dále popsané jako případové studie. Tato závěrečná část bude dobře dokladovat efektivitu navržených řešení i praktickou užitečnost v kontextu definovaných reálných problémů. Integračním cílem projektu je významně prohloubit existující mezinárodní spolupráci, popř. navázat novou spolupráci výzkumných týmů VUT v Brně zabývajících se evolučními algoritmy s relevantními předními zahraničními pracovišti a realizovat s nimi výzkum vedoucí ke společným publikacím a novým vědeckým výsledkům. 

Description in English
The scientific aim of the project is to design advanced evolutionary algorithms (EA) that are applicable in the up to date complex engineering optimizing and designing problems. Another objective is to adapt such algorithms for different user-defined platforms, e.g. for powerful GPU (Graphic Processing Unit) or, on the other hand, for low-power embedded systems. The project is divided into three solution phases called Work Packages (WP1-3). Within the first phase, new and hybrid evolutionary algorithms will be designed and evaluated. The implementations of HPC (High Performance Computing) and embedded systems will be realized in the second phase, where the pre-defined efficiency (computational performance, scalability, energy efficiency) will be emphasized. Within the third phase, the practical applications, referred to as the case studies consequently, will be elaborated. This final phase will prove the efficiency of the proposed algorithms and practical applicability w.r.t. the predefined real tasks. The integration objective of the project is to evolve the existing international co-operation and establish new collaboration of the research teams within BUT working on evolutionary algorithms with leading scientific institutions abroad. The aim is to present common publications containing new scientific results.

Keywords
Nature-inspired optimalizace, evoluční algoritmy, výpočetní inteligence, klíčové základní technologie, mezinárodní spolupráce

Key words in English
Nature-inspired optimization, evolutionary algorithm; computational intelligence, key enabling technologies; international cooperation

LTC18053

Czech

Matoušek Radomil, prof. Ing., Ph.D. - principal person responsible
Bidlo Michal, doc. Ing., Ph.D. - fellow researcher
Vašíček Zdeněk, doc. Ing., Ph.D. - fellow researcher

Institute of Automation and Computer Science
- responsible department (19.10.2016 - not assigned)
Institute of Automation and Computer Science
- responsible department (26.5.2020 - not assigned)
Evolvable Hardware Research Group
- internal (19.10.2016 - 29.2.2020)
Department of Computer Systems
- co-beneficiary (19.10.2016 - 29.2.2020)
Institute of Automation and Computer Science
- beneficiary (19.10.2016 - 29.2.2020)

ŠKRABÁNEK, P.; MARTÍNKOVÁ, N.: TGV; TGV methodology MATLAB implementation. https://www.sciencedirect.com/science/article/pii/S0141938222001044#mmc1. URL: https://www.sciencedirect.com/science/article/pii/S0141938222001044#mmc1. (software)
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ŠKRABÁNEK, P.; MARTÍNKOVÁ, N. Tuning of grayscale computer vision systems. DISPLAYS, 2022, no. 74, p. 102286-102286. ISSN: 0141-9382.
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ŠKRABÁNEK, P.; MARTÍNKOVÁ, N. Algorithm 1017: fuzzyreg: An R Package for Fitting Fuzzy Regression Models. ACM TRANSACTIONS ON MATHEMATICAL SOFTWARE, 2021, vol. 47, no. 3, p. 1-18. ISSN: 0098-3500.
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KLIMEŠ, L.; KESLER, R.; CHARVÁT, P. Metaheuristic design optimization of the air-pcm thermal storage unit for solar air systems. Chemical Engineering Transactions, 2020, vol. 81, no. 1, p. 205-210. ISSN: 2283-9216.
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HŮLKA, T.; MATOUŠEK, R.; DOBROVSKÝ, L.; DOSOUDILOVÁ, M.; NOLLE, L. Optimization of Snake-like Robot Locomotion Using GA: Serpenoid Design. Mendel Journal series, 2020, vol. 26 (2020), no. 1, p. 1-6. ISSN: 1803-3814.
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KŮDELA, J.; POPELA, P. Pool & Discard Algorithm for Chance Constrained Optimization Problems. IEEE Access, 2020, vol. 8, no. 1, p. 79397-79407. ISSN: 2169-3536.
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KŮDELA, J. Minimum-Volume Covering Ellipsoids: Improving the Efficiency of the Wolfe-Atwood Algorithm for Large-Scale Instances by Pooling and Batching. Mendel Journal series, 2019, vol. 25, no. 2, p. 19-26. ISSN: 1803-3814.
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MATOUŠEK, R.; HŮLKA, T.; DOBROVSKÝ, L.; KŮDELA, J. Sum Epsilon-Tube Error Fitness Function Design for GP Symbolic Regression: Preliminary Study. In 2019 International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO). IEEE, 2020. p. 78-83. ISBN: 978-1-7281-3572-4.
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MATOUŠEK, R.; HŮLKA, T. Stabilization of Higher Periodic Orbits of the Chaotic Logistic and Henon Maps using Meta-evolutionary Approaches. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. NEW YORK: IEEE, 2019. p. 1758-1765. ISBN: 978-1-7281-2153-6.
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ANSARI, M.; MRÁZEK, V.; COCKBURN, B.; SEKANINA, L.; VAŠÍČEK, Z.; HAN, J. Improving the Accuracy and Hardware Efficiency of Neural Networks Using Approximate Multipliers. IEEE Trans. on VLSI Systems., 2020, vol. 28, no. 2, p. 317-328. ISSN: 1063-8210.
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BADÁŇ, F.; SEKANINA, L. Optimizing Convolutional Neural Networks for Embedded Systems By Means of Neuroevolution. In Theory and Practice of Natural Computing. LNCS 11934. Cham: Springer International Publishing, 2019. p. 109-121. ISBN: 978-3-030-34499-3.
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KLIMEŠ, L.; KOZUBÍK, L.; CHARVÁT, P. Computational design optimization of PCM-based attenuator of fluid temperature fluctuations. In Proceedings of ASME IMECE 2019. ASME, 2019. p. 1-8. ISBN: 978-0-7918-5945-2.
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KŮDELA, J.; ŠOMPLÁK, R.; NEVRLÝ, V. Strategic Multi-Stage Planning of Waste Processing Infrastructure. Chemical Engineering Transactions, 2019, vol. 76, no. 1, p. 1261-1266. ISSN: 2283-9216.
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BIDLO, M. Comparison of Evolutionary Development of Cellular Automata Using Various Representations. Mendel Journal series, 2019, vol. 2019, no. 1, p. 95-102. ISSN: 1803-3814.
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BIDLO, M.; KORGO, J. Ant Colony Optimisation for Performing Computational Task in Cellular Automata. Mendel Journal series, 2019, vol. 25, no. 1, p. 147-156. ISSN: 1803-3814.
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KOCNOVÁ, J.; VAŠÍČEK, Z. Impact of subcircuit selection on the efficiency of CGP-based optimization of gate-level circuits. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York: Association for Computing Machinery, 2019. p. 377-378. ISBN: 978-1-4503-6748-6.
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BIDLO, M. Evolution of Cellular Automata Development Using Various Representations. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. Praha: Association for Computing Machinery, 2019. p. 107-108. ISBN: 978-1-4503-6748-6.
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MATOUŠEK, R.; DOBROVSKÝ, L.; KŮDELA, J. The quadratic assignment problem: metaheuristic optimization using HC12 algorithm. In GECCO '19 Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, NY, USA: ACM, 2019. p. 153-154. ISBN: 978-1-4503-6748-6.
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KŮDELA, J.; ŠOMPLÁK, R.; NEVRLÝ, V.; LIPOVSKÝ, T.; SMEJKALOVÁ, V.; DOBROVSKÝ, L. Multi-objective strategic waste transfer station planning. Journal of Cleaner Production, 2019, vol. 230, no. 1 September, p. 1294-1304. ISSN: 0959-6526.
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KONČAL, O.; SEKANINA, L. Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming. In Genetic Programming 22nd European Conference, EuroGP 2019. Cham: Springer International Publishing, 2019. p. 98-113. ISBN: 978-3-030-16669-4.
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Responsibility: Matoušek Radomil, prof. Ing., Ph.D.