[{"publication_status":"published","publist_id":"6206","file":[{"file_name":"2016_jocs_ewa.pdf","file_id":"5842","relation":"main_file","creator":"dernst","date_updated":"2019-01-18T08:43:16Z","access_level":"open_access","success":1,"content_type":"application/pdf","date_created":"2019-01-18T08:43:16Z","file_size":1083911}],"publication_identifier":{"issn":["1877-7503"]},"title":"A multi objective memetic inverse solver reinforced by local optimization methods","date_created":"2018-12-11T11:50:26Z","oa_version":"Submitted Version","doi":"10.1016/j.jocs.2016.06.007","oa":1,"author":[{"full_name":"Gajda-Zagorska, Ewa P","last_name":"Gajda-Zagorska","first_name":"Ewa P","id":"47794CF0-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Schaefer, Robert","last_name":"Schaefer","first_name":"Robert"},{"first_name":"Maciej","last_name":"Smołka","full_name":"Smołka, Maciej"},{"full_name":"Pardo, David","last_name":"Pardo","first_name":"David"},{"first_name":"Julen","last_name":"Alvarez Aramberri","full_name":"Alvarez Aramberri, Julen"}],"volume":18,"month":"01","department":[{"_id":"ChWo"}],"article_processing_charge":"No","publisher":"Elsevier","external_id":{"isi":["000393528700009"]},"language":[{"iso":"eng"}],"_id":"1152","abstract":[{"lang":"eng","text":"We propose a new memetic strategy that can solve the multi-physics, complex inverse problems, formulated as the multi-objective optimization ones, in which objectives are misfits between the measured and simulated states of various governing processes. The multi-deme structure of the strategy allows for both, intensive, relatively cheap exploration with a moderate accuracy and more accurate search many regions of Pareto set in parallel. The special type of selection operator prefers the coherent alternative solutions, eliminating artifacts appearing in the particular processes. The additional accuracy increment is obtained by the parallel convex searches applied to the local scalarizations of the misfit vector. The strategy is dedicated for solving ill-conditioned problems, for which inverting the single physical process can lead to the ambiguous results. The skill of the selection in artifact elimination is shown on the benchmark problem, while the whole strategy was applied for identification of oil deposits, where the misfits are related to various frequencies of the magnetic and electric waves of the magnetotelluric measurements. 2016 Elsevier B.V."}],"quality_controlled":"1","type":"journal_article","day":"01","citation":{"apa":"Gajda-Zagorska, E. P., Schaefer, R., Smołka, M., Pardo, D., &#38; Alvarez Aramberri, J. (2017). A multi objective memetic inverse solver reinforced by local optimization methods. <i>Journal of Computational Science</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jocs.2016.06.007\">https://doi.org/10.1016/j.jocs.2016.06.007</a>","ieee":"E. P. Gajda-Zagorska, R. Schaefer, M. Smołka, D. Pardo, and J. Alvarez Aramberri, “A multi objective memetic inverse solver reinforced by local optimization methods,” <i>Journal of Computational Science</i>, vol. 18. Elsevier, pp. 85–94, 2017.","short":"E.P. Gajda-Zagorska, R. Schaefer, M. Smołka, D. Pardo, J. Alvarez Aramberri, Journal of Computational Science 18 (2017) 85–94.","mla":"Gajda-Zagorska, Ewa P., et al. “A Multi Objective Memetic Inverse Solver Reinforced by Local Optimization Methods.” <i>Journal of Computational Science</i>, vol. 18, Elsevier, 2017, pp. 85–94, doi:<a href=\"https://doi.org/10.1016/j.jocs.2016.06.007\">10.1016/j.jocs.2016.06.007</a>.","chicago":"Gajda-Zagorska, Ewa P, Robert Schaefer, Maciej Smołka, David Pardo, and Julen Alvarez Aramberri. “A Multi Objective Memetic Inverse Solver Reinforced by Local Optimization Methods.” <i>Journal of Computational Science</i>. Elsevier, 2017. <a href=\"https://doi.org/10.1016/j.jocs.2016.06.007\">https://doi.org/10.1016/j.jocs.2016.06.007</a>.","ista":"Gajda-Zagorska EP, Schaefer R, Smołka M, Pardo D, Alvarez Aramberri J. 2017. A multi objective memetic inverse solver reinforced by local optimization methods. Journal of Computational Science. 18, 85–94.","ama":"Gajda-Zagorska EP, Schaefer R, Smołka M, Pardo D, Alvarez Aramberri J. A multi objective memetic inverse solver reinforced by local optimization methods. <i>Journal of Computational Science</i>. 2017;18:85-94. doi:<a href=\"https://doi.org/10.1016/j.jocs.2016.06.007\">10.1016/j.jocs.2016.06.007</a>"},"has_accepted_license":"1","intvolume":"        18","ddc":["000"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2017","date_published":"2017-01-01T00:00:00Z","file_date_updated":"2019-01-18T08:43:16Z","publication":"Journal of Computational Science","date_updated":"2025-07-10T11:50:11Z","isi":1,"status":"public","page":"85 - 94","scopus_import":"1"},{"article_processing_charge":"No","publisher":"Elsevier","acknowledgement":"The work presented in this paper was partially supported by Polish National Science Centre grant nos. DEC-2012/05/N/ST6/03433 and DEC-2011/03/B/ST6/01393. Radosław Łazarz was supported by Polish National Science Centre grant no. DEC-2013/10/M/ST6/00531.","doi":"10.1016/j.jocs.2016.03.004","author":[{"last_name":"Łazarz","full_name":"Łazarz, Radosław","first_name":"Radosław"},{"first_name":"Michał","last_name":"Idzik","full_name":"Idzik, Michał"},{"first_name":"Konrad","full_name":"Gądek, Konrad","last_name":"Gądek"},{"last_name":"Gajda-Zagorska","full_name":"Gajda-Zagorska, Ewa P","id":"47794CF0-F248-11E8-B48F-1D18A9856A87","first_name":"Ewa P"}],"volume":17,"department":[{"_id":"ChWo"}],"month":"11","title":"Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization","date_created":"2018-12-11T11:50:22Z","oa_version":"None","publist_id":"6217","publication_status":"published","status":"public","page":"249 - 260","issue":"1","scopus_import":"1","date_updated":"2025-09-22T14:11:23Z","publication":"Journal of Computational Science","isi":1,"intvolume":"        17","year":"2016","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_published":"2016-11-01T00:00:00Z","language":[{"iso":"eng"}],"_id":"1141","abstract":[{"lang":"eng","text":"In this paper we introduce the Multiobjective Optimization Hierarchic Genetic Strategy with maturing (MO-mHGS), a meta-algorithm that performs evolutionary optimization in a hierarchy of populations. The maturing mechanism improves growth and reduces redundancy. The performance of MO-mHGS with selected state-of-the-art multiobjective evolutionary algorithms as internal algorithms is analysed on benchmark problems and their modifications for which single fitness evaluation time depends on the solution accuracy. We compare the proposed algorithm with the Island Model Genetic Algorithm as well as with single-deme methods, and discuss the impact of internal algorithms on the MO-mHGS meta-algorithm. © 2016 Elsevier B.V."}],"external_id":{"isi":["000390625600021"]},"type":"journal_article","quality_controlled":"1","day":"01","citation":{"chicago":"Łazarz, Radosław, Michał Idzik, Konrad Gądek, and Ewa P Gajda-Zagorska. “Hierarchic Genetic Strategy with Maturing as a Generic Tool for Multiobjective Optimization.” <i>Journal of Computational Science</i>. Elsevier, 2016. <a href=\"https://doi.org/10.1016/j.jocs.2016.03.004\">https://doi.org/10.1016/j.jocs.2016.03.004</a>.","ista":"Łazarz R, Idzik M, Gądek K, Gajda-Zagorska EP. 2016. Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization. Journal of Computational Science. 17(1), 249–260.","ama":"Łazarz R, Idzik M, Gądek K, Gajda-Zagorska EP. Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization. <i>Journal of Computational Science</i>. 2016;17(1):249-260. doi:<a href=\"https://doi.org/10.1016/j.jocs.2016.03.004\">10.1016/j.jocs.2016.03.004</a>","apa":"Łazarz, R., Idzik, M., Gądek, K., &#38; Gajda-Zagorska, E. P. (2016). Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization. <i>Journal of Computational Science</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jocs.2016.03.004\">https://doi.org/10.1016/j.jocs.2016.03.004</a>","short":"R. Łazarz, M. Idzik, K. Gądek, E.P. Gajda-Zagorska, Journal of Computational Science 17 (2016) 249–260.","ieee":"R. Łazarz, M. Idzik, K. Gądek, and E. P. Gajda-Zagorska, “Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization,” <i>Journal of Computational Science</i>, vol. 17, no. 1. Elsevier, pp. 249–260, 2016.","mla":"Łazarz, Radosław, et al. “Hierarchic Genetic Strategy with Maturing as a Generic Tool for Multiobjective Optimization.” <i>Journal of Computational Science</i>, vol. 17, no. 1, Elsevier, 2016, pp. 249–60, doi:<a href=\"https://doi.org/10.1016/j.jocs.2016.03.004\">10.1016/j.jocs.2016.03.004</a>."}}]