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Doctor Ciprian Zavoianu


Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. (2020)
Conference Proceeding
ZĂVOIANU, A.-C., LACROIX, B. and MCCALL, J. 2020. Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, The Netherlands. Theoretical computer science and general issues, 12269. Cham; Springer, part 1, pages 287-300. Available from: https://doi.org/10.1007/978-3-030-58112-1_20

We propose a new class of multi-objective benchmark problems on which we analyse the performance of four well established multi-objective evolutionary algorithms (MOEAs) – each implementing a different search paradigm – by comparing run-time converge... Read More about Comparative run-time performance of evolutionary algorithms on multi-objective interpolated continuous optimisation problems..

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. (2020)
Journal Article
LUGHOFER, E., ZAVOIANU, A.-C., POLLAK, R., PRATAMA, M., MEYER-HEYE, P., ZÖRRER, H., EITZINGER, C. and RADAUER, T. 2020. On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks. Information sciences [online], 537, 425-451. Available from: https://doi.org/10.1016/j.ins.2020.06.034

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detecti... Read More about On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks..

On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks. (2020)
Conference Proceeding
ZĂVOIANU, A.-C., KITZBERGER, M., BRAMERDORFER, G. and SAMINGER-PLATZ, S. 2020. On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: EUROCAST 2019: revised selected papers from the proceedings of the 17th International conference on computer aided systems theory (EUROCAST 2019), 17-22 February 2019, Las Palmas de Gran Canaria, Spain. Part I. Lecture notes in computer science, 12013. Cham: Springer [online], pages 319-326. Available from: https://doi.org/10.1007/978-3-030-45093-9_39

We describe initial attempts to model the dynamic thermal behavior of electrical machines by evaluating the ability of linear and non-linear (regression) modeling techniques to replicate the performance of simulations carried out using a lumped param... Read More about On modeling the dynamic thermal behavior of electrical machines using genetic programming and artificial neural networks..

Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. (2019)
Conference Proceeding
ZAVOIANU, A.-C., SAMINGER-PLATZ, S. and AMRHEIN, W. 2019. Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, New Zealand. Piscataway: IEEE [online], article number 8790133, pages 3078-3085. Available from: https://doi.org/10.1109/CEC.2019.8790133

We describe and compare two steady state asynchronous parallelization variants for DECMO2++, a recently proposed multi-objective coevolutionary solver that generally displays a robust run-time convergence behavior. The two asynchronous variants were... Read More about Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver..

Potential identification and industrial evaluation of an integrated design automation workflow. (2019)
Journal Article
ENTNER, D., PRANTE, T., VOSGIEN, T., ZAVOIANU, A.-C., SAMINGER-PLATZ, S., SCHWARZ, M. and FINK, K. 2019. Potential identification and industrial evaluation of an integrated design automation workflow. Journal of engineering, design and technology [online], 17(6), pages 1085-1109. Available from: https://doi.org/10.1108/jedt-06-2018-0096

Purpose - The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design sp... Read More about Potential identification and industrial evaluation of an integrated design automation workflow..

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models. (2019)
Journal Article
LUGHOFER, E., ZAVOIANU, A.-C., POLLAK, R., PRATAMA, M., MEYER-HEYE, P., ZÖRRER, H., EITZINGER, C. and RADAUER, T. 2019. Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models. Journal of process control [online], 76, pages 27-45. Available from: https://doi.org/10.1016/j.jprocont.2019.02.005

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potent... Read More about Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models..

Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm. (2018)
Conference Proceeding
ZǍVOIANU, A.-C., SAMINGER-PLATZ, S., LUGHOFER, E. and AMRHEIN, W. 2018. Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm. In Aguirre, H. (ed.) Proceedings of the 2018 Genetic and evolutionary computation conference (GECCO'18), 15-19 July 2018, Kyoto, Japan. New York: Association for Computing Machinery [online], pages 793-800. Available from: https://doi.org/10.1145/3205455.3205549

We describe two enhancements that significantly improve the rapid convergence behavior of DECM02 - a previously proposed robust coevolutionary algorithm that integrates three different multi-objective space exploration paradigms: differential evoluti... Read More about Two enhancements for improving the convergence speed of a robust multi-objective coevolutionary algorithm..

Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems. (2015)
Journal Article
ZĂVOIANU, A.-C., LUGHOFER, E., KOPPELSTÄTTER, W., WEIDENHOLZER, G., AMRHEIN, W. and KLEMENT, E.P. 2015. Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems. Knowledge-based systems [online], 87, pages 47-60. Available from: https://doi.org/10.1016/j.knosys.2015.05.029

In the last two decades, multi-objective evolutionary algorithms (MOEAs) have become ever more used in scientific and industrial decision support and decision making contexts the require an a posteriori articulation of preference. The present work is... Read More about Performance comparison of generational and steady-state asynchronous multi-objective evolutionary algorithms for computationally-intensive problems..

DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm. (2014)
Journal Article
ZĂVOIANU, A.-C., LUGHOFER, E., BRAMERDORFER, G., AMRHEIN, W. and KLEMENT, E.P. 2015. DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm. Soft computing [online], 19(12), pages 3551-3569. Available from: https://doi.org/10.1007/s00500-014-1308-7

We describe a hybrid and adaptive coevolutionary optimization method that can efficiently solve a wide range of multi-objective optimization problems (MOOPs) as it successfully combines positive traits from three main classes of multi-objective evolu... Read More about DECMO2: a robust hybrid and adaptive multi-objective evolutionary algorithm..

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