---
_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.
article_processing_charge: No
author:
- first_name: Ewa P
full_name: Gajda-Zagorska, Ewa P
id: 47794CF0-F248-11E8-B48F-1D18A9856A87
last_name: Gajda-Zagorska
- first_name: Robert
full_name: Schaefer, Robert
last_name: Schaefer
- first_name: Maciej
full_name: Smołka, Maciej
last_name: Smołka
- first_name: David
full_name: Pardo, David
last_name: Pardo
- first_name: Julen
full_name: Alvarez Aramberri, Julen
last_name: Alvarez Aramberri
citation:
ama: Gajda-Zagorska EP, Schaefer R, Smołka M, Pardo D, Alvarez Aramberri J. A multi
objective memetic inverse solver reinforced by local optimization methods. Journal
of Computational Science. 2017;18:85-94. doi:10.1016/j.jocs.2016.06.007
apa: Gajda-Zagorska, E. P., 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. Elsevier. https://doi.org/10.1016/j.jocs.2016.06.007
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.” Journal of Computational Science. Elsevier,
2017. https://doi.org/10.1016/j.jocs.2016.06.007.
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,”
Journal of Computational Science, vol. 18. Elsevier, pp. 85–94, 2017.
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.
mla: Gajda-Zagorska, Ewa P., et al. “A Multi Objective Memetic Inverse Solver Reinforced
by Local Optimization Methods.” Journal of Computational Science, vol.
18, Elsevier, 2017, pp. 85–94, doi:10.1016/j.jocs.2016.06.007.
short: E.P. Gajda-Zagorska, R. Schaefer, M. Smołka, D. Pardo, J. Alvarez Aramberri,
Journal of Computational Science 18 (2017) 85–94.
date_created: 2018-12-11T11:50:26Z
date_published: 2017-01-01T00:00:00Z
date_updated: 2023-09-20T11:29:44Z
day: '01'
ddc:
- '000'
department:
- _id: ChWo
doi: 10.1016/j.jocs.2016.06.007
external_id:
isi:
- '000393528700009'
file:
- access_level: open_access
content_type: application/pdf
creator: dernst
date_created: 2019-01-18T08:43:16Z
date_updated: 2019-01-18T08:43:16Z
file_id: '5842'
file_name: 2016_jocs_ewa.pdf
file_size: 1083911
relation: main_file
success: 1
file_date_updated: 2019-01-18T08:43:16Z
has_accepted_license: '1'
intvolume: ' 18'
isi: 1
language:
- iso: eng
month: '01'
oa: 1
oa_version: Submitted Version
page: 85 - 94
publication: Journal of Computational Science
publication_identifier:
issn:
- '18777503'
publication_status: published
publisher: Elsevier
publist_id: '6206'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A multi objective memetic inverse solver reinforced by local optimization methods
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 18
year: '2017'
...
---
_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.
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.
author:
- first_name: Radosław
full_name: Łazarz, Radosław
last_name: Łazarz
- first_name: Michał
full_name: Idzik, Michał
last_name: Idzik
- first_name: Konrad
full_name: Gądek, Konrad
last_name: Gądek
- first_name: Ewa P
full_name: Gajda-Zagorska, Ewa P
id: 47794CF0-F248-11E8-B48F-1D18A9856A87
last_name: Gajda-Zagorska
citation:
ama: Łazarz R, Idzik M, Gądek K, Gajda-Zagorska EP. Hierarchic genetic strategy
with maturing as a generic tool for multiobjective optimization. Journal of
Computational Science. 2016;17(1):249-260. doi:10.1016/j.jocs.2016.03.004
apa: Łazarz, R., Idzik, M., Gądek, K., & Gajda-Zagorska, E. P. (2016). Hierarchic
genetic strategy with maturing as a generic tool for multiobjective optimization.
Journal of Computational Science. Elsevier. https://doi.org/10.1016/j.jocs.2016.03.004
chicago: Łazarz, Radosław, Michał Idzik, Konrad Gądek, and Ewa P Gajda-Zagorska.
“Hierarchic Genetic Strategy with Maturing as a Generic Tool for Multiobjective
Optimization.” Journal of Computational Science. Elsevier, 2016. https://doi.org/10.1016/j.jocs.2016.03.004.
ieee: R. Łazarz, M. Idzik, K. Gądek, and E. P. Gajda-Zagorska, “Hierarchic genetic
strategy with maturing as a generic tool for multiobjective optimization,” Journal
of Computational Science, vol. 17, no. 1. Elsevier, pp. 249–260, 2016.
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.
mla: Łazarz, Radosław, et al. “Hierarchic Genetic Strategy with Maturing as a Generic
Tool for Multiobjective Optimization.” Journal of Computational Science,
vol. 17, no. 1, Elsevier, 2016, pp. 249–60, doi:10.1016/j.jocs.2016.03.004.
short: R. Łazarz, M. Idzik, K. Gądek, E.P. Gajda-Zagorska, Journal of Computational
Science 17 (2016) 249–260.
date_created: 2018-12-11T11:50:22Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2021-01-12T06:48:35Z
day: '01'
department:
- _id: ChWo
doi: 10.1016/j.jocs.2016.03.004
intvolume: ' 17'
issue: '1'
language:
- iso: eng
month: '11'
oa_version: None
page: 249 - 260
publication: Journal of Computational Science
publication_status: published
publisher: Elsevier
publist_id: '6217'
quality_controlled: '1'
scopus_import: 1
status: public
title: Hierarchic genetic strategy with maturing as a generic tool for multiobjective
optimization
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2016'
...