{"issue":"1","publist_id":"6217","publisher":"Elsevier","oa_version":"None","quality_controlled":"1","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.","language":[{"iso":"eng"}],"intvolume":"        17","author":[{"last_name":"Łazarz","full_name":"Łazarz, Radosław","first_name":"Radosław"},{"last_name":"Idzik","full_name":"Idzik, Michał","first_name":"Michał"},{"full_name":"Gądek, Konrad","first_name":"Konrad","last_name":"Gądek"},{"full_name":"Gajda-Zagorska, Ewa P","first_name":"Ewa P","last_name":"Gajda-Zagorska","id":"47794CF0-F248-11E8-B48F-1D18A9856A87"}],"publication_status":"published","month":"11","department":[{"_id":"ChWo"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","doi":"10.1016/j.jocs.2016.03.004","title":"Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization","date_updated":"2021-01-12T06:48:35Z","date_published":"2016-11-01T00:00:00Z","abstract":[{"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.","lang":"eng"}],"date_created":"2018-12-11T11:50:22Z","page":"249 - 260","publication":"Journal of Computational Science","_id":"1141","status":"public","year":"2016","volume":17,"day":"01","scopus_import":1,"citation":{"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>.","short":"R. Łazarz, M. Idzik, K. Gądek, E.P. Gajda-Zagorska, Journal of Computational Science 17 (2016) 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>","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.","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."},"type":"journal_article"}