Project Details
Pokročilé metody nature-inspired optimalizačních algoritmů a HPC implementace pro řešení reálných aplikací
Project Period: 1. 6. 2018 – 29. 2. 2020
Project Type: grant
Code: LTC18053
Agency: Ministerstvo školství, mládeže a tělovýchovy ČR
Program: INTER-EXCELLENCE - Podprogram INTER-COST
Nature-inspired optimization, evolutionary algorithm; computational intelligence,
key enabling technologies; international cooperation
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.
Bidlo Michal, doc. Ing., Ph.D. (DCSY)
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
2020
- 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. Detail
2019
- 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. Detail - 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. Detail - 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. Detail - 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. Detail - 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. Detail - KOCNOVÁ, J.; VAŠÍČEK, Z. Towards a Scalable EA-based Optimization of Digital Circuits. In Genetic Programming 22nd European Conference, EuroGP 2019. Cham: Springer International Publishing, 2019.
p. 81-97. ISBN: 978-3-030-16669-4. Detail - 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. Detail
2018
- GROCHOL, D.; SEKANINA, L. Fast Reconfigurable Hash Functions for Network Flow Hashing in FPGAs. In Proceedings of the 2018 NASA/ESA Conference on Adaptive Hardware and Systems. Edinburgh: Institute of Electrical and Electronics Engineers, 2018.
p. 257-263. ISBN: 978-1-5386-7753-7. Detail - MRÁZEK, V.; VAŠÍČEK, Z.; SEKANINA, L. Design of Quality-Configurable Approximate Multipliers Suitable for Dynamic Environment. In Proceedings of the 2018 NASA/ESA Conference on Adaptive Hardware and Systems. Edinburgh: Institute of Electrical and Electronics Engineers, 2018.
p. 264-271. ISBN: 978-1-5386-7753-7. Detail - SEKANINA, L.; MRÁZEK, V.; VAŠÍČEK, Z. Design Space Exploration for Approximate Implementations of Arithmetic Data Path Primitives. In 25th IEEE International Conference on Electronics Circuits and Systems (ICECS). Bordeaux: IEEE Circuits and Systems Society, 2018.
p. 377-380. ISBN: 978-1-5386-9562-3. Detail
2022
- TGV methodology MATLAB implementation, software, 2022
Authors: ŠKRABÁNEK, P.; MARTÍNKOVÁ, N.