Moduł oferowany także w ramach programów studiów:
Informacje ogólne:
Nazwa:
Effective algorithms of inverse analysis. Is it possible to beat Holmes at his own game?
Tok studiów:
2019/2020
Kod:
ZSDA-3-0245-s
Wydział:
Szkoła Doktorska AGH
Poziom studiów:
Studia III stopnia
Specjalność:
-
Kierunek:
Szkoła Doktorska AGH
Semestr:
0
Profil:
Ogólnoakademicki (A)
Język wykładowy:
Angielski
Forma studiów:
Stacjonarne
Strona www:
 
Prowadzący moduł:
prof. dr hab. inż. Schaefer Robert (schaefer@agh.edu.pl)
Dyscypliny:
Moduł multidyscyplinarny
Treści programowe zapewniające uzyskanie efektów uczenia się dla modułu zajęć

We show how to design the effective strategies solving ill conditioned inverse probles by coupling artificial intelligence with a modern numerical analysis. “Holmes deduction” allows for improoving both: the solution’s quality itself and on-line the efficiency of searching process.

Opis efektów uczenia się dla modułu zajęć
Kod MEU Student, który zaliczył moduł zajęć zna i rozumie/potrafi/jest gotów do Powiązania z KEU Sposób weryfikacji i oceny efektów uczenia się osiągniętych przez studenta w ramach poszczególnych form zajęć i dla całego modułu zajęć
Wiedza: zna i rozumie
M_W001 Student knows the scientific background and motivation for studying inverse problems, their formal definition and taxonomies. SDA3A_W02 Prezentacja,
Studium przypadków ,
Aktywność na zajęciach
M_W002 Student possesses the knowledge about the methods of solving inverse problems and their applicability in case of ill conditioning. SDA3A_W03, SDA3A_W02 Prezentacja,
Studium przypadków ,
Aktywność na zajęciach
M_W003 Student possesses the knowledge about the modern algorithms devoted for solving ill conditioned inverse problems with a special emphasis on the stochastic memetic strategies. SDA3A_W03, SDA3A_W02 Prezentacja,
Studium przypadków ,
Aktywność na zajęciach
M_W004 Student possesses the specific knowledge of designing and executing inverse analysis systems in a multiprocessor and distributed computer environments. SDA3A_W03 Prezentacja,
Studium przypadków ,
Aktywność na zajęciach
Umiejętności: potrafi
M_U001 Student is able to perform the individual literature study in area of inverse analysis algorithms and their applications. SDA3A_U01 Prezentacja,
Udział w dyskusji,
Studium przypadków ,
Aktywność na zajęciach
M_U002 Student can apply the archived knowledge for designing and implementing systems of inverse analysis. SDA3A_U06 Studium przypadków ,
Aktywność na zajęciach
M_U003 Student can utilize the modern computer facilities for implementing for executing inverse analysis in a multiprocessor distributed environment. SDA3A_U06 Studium przypadków ,
Aktywność na zajęciach
Kompetencje społeczne: jest gotów do
M_K001 Student understands the necessity of continuous studying and learning algorithms, methods and computer systems solving difficult inverse problems in various disciplines. SDA3A_K01 Prezentacja,
Studium przypadków ,
Aktywność na zajęciach
Liczba godzin zajęć w ramach poszczególnych form zajęć:
SUMA (godz.)
Wykład
Ćwicz. aud
Ćwicz. lab
Ćw. proj.
Konw.
Zaj. sem.
Zaj. prakt
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
45 30 0 0 0 0 15 0 0 0 0 0
Matryca kierunkowych efektów uczenia się w odniesieniu do form zajęć i sposobu zaliczenia, które pozwalają na ich uzyskanie
Kod MEU Student, który zaliczył moduł zajęć zna i rozumie/potrafi/jest gotów do Forma zajęć dydaktycznych
Wykład
Ćwicz. aud
Ćwicz. lab
Ćw. proj.
Konw.
Zaj. sem.
Zaj. prakt
Zaj. terenowe
Zaj. warsztatowe
Prace kontr. przejść.
Lektorat
Wiedza
M_W001 Student knows the scientific background and motivation for studying inverse problems, their formal definition and taxonomies. + - - - - + - - - - -
M_W002 Student possesses the knowledge about the methods of solving inverse problems and their applicability in case of ill conditioning. + - - - - + - - - - -
M_W003 Student possesses the knowledge about the modern algorithms devoted for solving ill conditioned inverse problems with a special emphasis on the stochastic memetic strategies. + - - - - + - - - - -
M_W004 Student possesses the specific knowledge of designing and executing inverse analysis systems in a multiprocessor and distributed computer environments. + - - - - + - - - - -
Umiejętności
M_U001 Student is able to perform the individual literature study in area of inverse analysis algorithms and their applications. + - - - - + - - - - -
M_U002 Student can apply the archived knowledge for designing and implementing systems of inverse analysis. + - - - - + - - - - -
M_U003 Student can utilize the modern computer facilities for implementing for executing inverse analysis in a multiprocessor distributed environment. + - - - - + - - - - -
Kompetencje społeczne
M_K001 Student understands the necessity of continuous studying and learning algorithms, methods and computer systems solving difficult inverse problems in various disciplines. + - - - - + - - - - -
Nakład pracy studenta (bilans punktów ECTS)
Forma aktywności studenta Obciążenie studenta
Sumaryczne obciążenie pracą studenta 67 godz
Punkty ECTS za moduł 4 ECTS
Udział w zajęciach dydaktycznych/praktyka 45 godz
Przygotowanie do zajęć 10 godz
przygotowanie projektu, prezentacji, pracy pisemnej, sprawozdania 10 godz
Egzamin lub kolokwium zaliczeniowe 2 godz
Szczegółowe treści kształcenia w ramach poszczególnych form zajęć (szczegółowy program wykładów i pozostałych zajęć)
Wykład (30h):

I. Preliminaries:

I.1. Simple examples of inverse problems.
I.2. Abstract definition of inverse problem, forward problem, observation, admissible parameter set, inverse problem operator.
I.3. Simple taxonomy of inverse problems:
• Initial inverse problems (we try to restore the past),
• Boundary inverse problems (we try to explore the word from the closed car)
• Parametric inverse problems (we try to recognize the black box contents without
looking inside)
I.4. Two ways to solve:
• Inverting the forward operator
• Minimizing misfit between the observation and a simulated forward solution.

II. Case studies:

II.1. Heat conductivity identification,
II.2. Defektoscopy and SFIL problem in micro chip manufacturing,
Structure Health Monitoring (SHM),
II.3. DC/AC Logging data inversion,
II.4. Magnetotelluric data inversion,
II.5. Elastography Inverse Problem of Tumor Identification,
II.6. Tumor identification based on inverse scattering approach,
II.7. Destroying cancer by the controlled hyperthermia process,
II.8. Star location on the base of gravitational lensing images.

III. Algorithms and strategies review:

III.1. Adaptive solvers of forward problems
III.2. Simple inversion algorithms (IFEM)
III.3. Regularization algorithms:
• Tikhonov compliment,
• Stochastic Regularization – Simulated Aneealing
III.4. Deterministic global optimization,
III.5. Robust Optimization,
III.6. Uncertain Optimization,
III.7. Stochastic global optimization,

IV. Analyzing and profiling algorithms and strategies solving ill conditioned inverse problems

IV.1. Well – and ill – posed problems in sense of Hadamard. Conditionally well posed problems in sense of Tikchonov.
IV.2. Local and global taxonomies of ill-conditionning, multimodality and insensitivity of solutions.
IV.3. Ill conditioning causes:
• Immanently ill conditioned problems (e.g. by symmetries),
• Insufficient and incorrect information (observation) of a forward solution,
• Numerical errors.
IV.4. Misfit regularization versus modality and insensitivity analyzis (finding the sets of insensitivity for solutions).
IV.5. Applying adaptive memetic algorithms – how to involve deduction in advanced stochastic search?
IV.6. Hierarchic Memetic Search coupled with adaptive Finite Element method (hp-HMS) for effective modality analysis.
IV.7. Clustered Genetic Search (CGS) as a way for insensitivity analysis.
IV.8. Important asymptotic features of single- and multi-deme stochastic searches.
IV.9. Stopping conditions and methods of the computational cost evaluation for stochastic strategies of solving inverse problems.

V. Implementing and executing inverse solvers in modern computer environments.

V.1. Concurrency in forward solvers. Domain decomposition, SBS-PCG and GMRES algorithms. Concurrent matrix factorization.
V.2. Concurrency in stochastic searches. Parallel misfit evaluation.
V.3. Scheduling or complex inverse solvers in cluster-like environments.

Zajęcia seminaryjne (15h):

Ideas and methods presented at the lecture will be discussed and consolidated. Students will prepare presentations about selected algorithms and strategies of solving inverse problems, possibly close to their main research area. Presentations will be based on a monographs, recent papers and research reports. Modifications and profiling of the selected strategies towards inverse problems appearing in student’s research would be also invented and verified.

Pozostałe informacje
Metody i techniki kształcenia:
  • Wykład: Oral and multimedial presentation.
  • Zajęcia seminaryjne: Inpsiring and discussing student's presentations.
Warunki i sposób zaliczenia poszczególnych form zajęć, w tym zasady zaliczeń poprawkowych, a także warunki dopuszczenia do egzaminu:

Students assessment at the lectures will base of their activity and the final test result.
Students assessment at the seminar will base of the quality of their presentation.

Zasady udziału w zajęciach:
  • Wykład:
    – Obecność obowiązkowa: Nie
    – Zasady udziału w zajęciach: Participation is not obligatory, but the activity during lectures will be taken into account in the final assessment. Moreover, the knowledge presented at the lectures is necessary for passing final test.
  • Zajęcia seminaryjne:
    – Obecność obowiązkowa: Tak
    – Zasady udziału w zajęciach: Presence is obligatory. Students have to prepare short presentations based on the sellected research papers and reports.
Sposób obliczania oceny końcowej:

The final grade will be the arithmetic mean fom the grades obtained at the seminar and the lecture. The assessment of both components (lectures and seminar) have to be positive.

Sposób i tryb wyrównywania zaległości powstałych wskutek nieobecności studenta na zajęciach:

The students can overcompensate the missed parts of material in other terms accepted by the teacher. The reasons of their absence has to be formally motivated.

Wymagania wstępne i dodatkowe, z uwzględnieniem sekwencyjności modułów :

Mathematical analysis, algebra and probability theory.

Zalecana literatura i pomoce naukowe:

Basic literature:

1. Albert Tarantolla, Inverse Problem Theory, SIAM, Philadelphia 2005.
2. Billingsley P., Probability and Measure. John Willey and Sons. Cichaster, Brisbane, Toronto, 1979.
3. Birattari M., Tunning Metaheuristics. Springer 2009.
4. Engl .W., Hanke M., Neubauer A.: Regularization of inverse problems. Kluwer Academic Press 1996.
5. Guillaume Bal, Introduction to Inverse Problems, Columbia University, New York NY, 2012.
6. https://books.google.pl/books/about/Introduction_to_Inverse_Problems_in_Imag.html?id=CO2wLTkCtR0C&redir_esc=y
7. Jakubowski J., Sztencel R., Introduction to the Probability Theory (in Polish), Script 2004.
8. Jari Kaipo, Erkki Somersalo, Statistical and Computational Inverse Problems, Springer series:Applied Mathematical Sciences, Vol. 160, Springer 2005.
9. Kirsh A.: An introduction of the mathematical theory of inverse problems. Springer 1996.
10. Neri F., Cotta C., Moscato P. (eds), Handbook of Memetic Algorithms, Springer 2012.
11. Panos M. Pardalos, H. Edwin Romeijn; Handbook of Global Optimization, Volume 2 (Nonconvex Optimization and its Applications), Kluver Academic Publisher 2002.
12. Schaefer R., (with the chapter 6 written by Telega H.), Foundation of Global Genetic Optimization. Springer 2007.

Complementary literature:

1. Aous Abdo, Gravitational Lensing, http://www.pa.msu.edu/~abdo/Gravitational Lensing.pdf
2. Cabib E., Davini C., Chong-Quing Ru (1990): A problem in the optimal design of networks under transverse loading, Quarterly of Appl. Math. Vol. XLVIII, No. 2, 1990, pp. 251–263.
3. Cabib E., Schaefer R., Telega H. (1998): A Parallel Genetic Clustering for Inverse A. KhanProblems. Lecture Notes in Computer Science, vol. 1541, pp. 551–556, 1998.
4. Garibaldi L., Surace C., Holford K., Ostachowicz W.M., Damage Assesment of Structures, Trans. Tech. Publications, Zurich, Switzerland 1999.
5. Jakubowski J., Sztencel R., Introduction to the Probability Theory (in Polish), Script 2004.
6. Jolanta Dziatkiewicz, Wacław Kuś, Ewa Majchrzak, Tadeusz Burczyński, Łukasz Turchan, Bioinspired identification of parameters in microscale heat transfer. Int. J. Multiscale Comput. Eng. 2014 vol. 12 iss. 1, pp. 79-89
7. Marek Paruch, Hyperthermia process control induced by the electric field in order to destroy cancer, Acta of Bioengineering and Biomechanics, Vol. 16, No. 4, 2014, pp. 123-130, DOI: http://10.5277/ABB-00075-2014-02
8. Mark S Gockenbach, Baasansuren Jadamaba, Akhtar A. Khan, Christiane Tammer, Brian Winkler, Proximal Methods for the Elastography Inverse Problem of Tumor Identification Using an Equation rror Approach in Advances, Chapter 7 in Weimin Han, Stanisław Migórski, Mircea Sofonea eds. Variational and Hemivariational Inequaities, Springer 2015, http://books.google.pl/books?id=DNv5BwAAQBAJ&pg=PA175&lpg=PA175&dq=Tumor+tissue+identification+inverse+problem&source=bl&ots=4dCunTsDpf&sig=_Fq4EbKzjaM80jygvm3cL5jyifA&hl=pl&sa=X&ei=OeqfVeDkGebryAPR7IX4Cg&ved=0CDUQ6AEwAg#v=onepage&q=Tumor%20tissue%20identification%20inverse%20problem&f=false
9. Nariman Firoozy, Ahad Tavakoli, Breast tumour identification based on inverse scattering approach, IET Microwaves, Antennas & Propagation, DOI:10.1049/iet-map.2012.0618
10. Neri F., Cotta C., Moscato P. (eds), Handbook of Memetic Algorithms, Springer 2012.
11. Ramesh Narayan, Matthias Bartelman, Lectures on gravitational lensing, http://arXiv:astro-ph/9606001
12. Roland Potthast and Peter beim Graben, Inverse problems in Neural Field Theory, Siam J. Appl. Dynamical Systems, Vol. 8, No. 4, pp. 1405-1433.
13. Maciej Smołka; Memetic strategies and autonomous syetms for solving inverse problems; AGH Press, Krakow 2015.

Publikacje naukowe osób prowadzących zajęcia związane z tematyką modułu:

1. Schaefer R., Denkowski Z., Telega H.; On identification problems for prelinear filtration of ground water. Proc. of the VIII Conf. “Finite Elements in Fluids” Barcelona 1993, Vol. II, pp. 878-886.
2. Schaefer R., Migórski S., Telega H.; A simple solution of the prelinear filtration inverse problem. Proc. of the 11th Conf. “Computer Methods in Mechanics”, Kielce-Cedzyna 1993, Vol. II, pp. 617-624.
3. Schaefer R., Denkowski Z., Migórski S., Telega H.; Mathematical and computational aspects of inverse problems for nonlinear filtration process. Proc. of the Second International Symposium on Inverse Problems in Engineering Mechanics ISIP’94, Paris 1994, pp. 403-409.
4. Denkowski Z., Migórski S., Schaefer R., Telega H.; On inverse problems in fluid mechanics. Proc. of the 12th Conf. “Computer Methods in Mechanics”, Warsaw – Zegrze May 1995, WAT Press 2312/95, pp. 84-85.
5. Schaefer R., Denkowski Z., Migórski S., Telega H.; Inverse problem for the prelinear filtration of ground water. Computer Assisted Mechanics and Engineering Sciences (CAMES), Vol. 3, 1996, pp. 97-107.
6. Schaefer R., Telega H.; A hybrid approach to the hydraulic conductivity identification in earthen dams. Proc. of the 1st Conference “Evolutionary Algorithms” Murzasichle 12-15 June 1996, Wydawnictwa Politechniki Warszawskiej 1996, pp. 162-169.
7. Schaefer R., Cabib E.; Optimal pretraction design in network structures. Proc. of the Int. Conf. on Numerical Methods in Continuous Mechanics, High Tatras, Slovakia, 16-18th September, 1996, pp. 236–240.
8. Schaefer R., Cabib E.; Optimal pretraction design in network structures. Strojnicky Časopis (Mechanical Engineering), Vol. 48, No. 3, pp. 191–202, Bratislava 1997.
9. Cabib E., Schaefer R., Telega H.; A Parallel Genetic Clustering for Inverse Problems. Lecture Notes in Computer Science 1541, pp. 551-556, Springer 1998, (also communicated at the Workshop on Applied Parallel Computing in Large Scale Scientific and Industrial Problems PARA98, Umeá, Sweden, 1998).
10. Schaefer R., Telega H., Kołodziej J.; Stochastic Theory of the Genetic Algorithm. Proc. of the Workshop „Neural Networks, Genetic Algorithms, Fuzzy Sets”, pp. 89-98, Rzeszów, 28-29 May 1999 (invited paper).
11. Schaefer R., Telega H.; Advantages and Drawbacks of a Genetic Clustering Strategy. Proc. of the 3rd Conf. on Evolutionary Algorithms and Global Optimization, Potok Złoty, 25-28 May 1999, Wydawnictwa Politechniki Warszawskiej 1999, pp. 291-300.
12. Schaefer R., Telega H., Kołodziej J.; Genetic Algorithm as a Markov Dynamic System. Proc. of the Int. Conf. on Intelligent Techniques in Robotics, Control and Decision Making, Polish-Japanese Institute of Information Technology Press, Warsaw, 22-23 February 1999, pp. 82-85 (invited paper).
13. Schaefer R., Telega H.; Testing the Genetic Clustering with SGA evolutionary engine. Proc. of the 4th Conf. on Evolutionary Algorithms and Global Optimization, Lądek Zdrój, 5-8 June 2000, Wydawnictwa Politechniki Warszawskiej 2000, pp. 227-236.
14. Schaefer R., Kołodziej J., Gwizdała R., Wojtusiak J.; How simpletons can increase the community development – an attempt to hierarchical genetic computation. Proc. of the 4th Conf. on Evolutionary Algorithms and Global Optimization, Lądek Zdrój, 5-8 June 2000, Wydawnictwa Politechniki Warszawskiej 2000, pp. 187-198.
15. Schaefer R.; Adaptability and Self-adaptability in genetic global optimization. Proc. of the 1th Conf. „Methods of Artificial Intelligence in Mechanics and Mechanical Engineering”, Burczyński T., Cholewa W. (Eds.), Gliwice 2000.11.15-17, pp. 291-298 (invited paper).
16. Schaefer R., Jabłoński Z.J.; Set recognition by the measure transport method in genetic search. Proc. of the 5th Conf. on Evolutionary Algorithms and Global Optimization, Jastrzębia Góra, 30 May – 01 June 2001, Wydawnictwa Politechniki Warszawskiej 2001, pp. 196-200.
17. Schaefer R., Jabłoński Z.J.; On the convergence of sampling measures in the global genetic search. Lecture Notes in Computer Science, vol. 2328, pp. 593-600, Springer 2002.
18. Schaefer R.; Simple taxonomy of the genetic global optimization. Computer Assisted Mechanics and Engineering Sciences CAMES , Vol. 9, pp. 139-145, 2002.
19. Schaefer R. (with the chapter 6 written by Telega H.); Foundations of the genetic global optimization (Podstawy genetycznej optymalizacji globalnej). Jagiellonian University Press, Kraków 2002 (in Polish).
20. Schaefer R., Jabłoński Z.J.; How to gain more information from the evolving population? Part of the book: Evolutionary Computation and Global Optimization, Jarosław Arabas (Ed.), Wydawnictwa Politechniki Warszawskiej, Warszawa 2002, pp. 21-33.
21. Schaefer R.; Problems of the convergence of the genetic search (Zagadnienia zbieżności algorytmów genetycznych). in Tadeusiewicz R., Ligęza A., Szymkat M. (Eds.) Proc. of the 3rd Polish Conf. „Metody i Systemy Komputerowe w Badaniach Naukowych i Projektowaniu Inżynierskim”, Kraków, November 2001.11.19-21 (in Polish) pp. 19-24.
22. Schaefer R., Adamska-Piskorz K.; Effective attractor recognition methods based on genetic sampling measure. Proc. of the Second International Conference on Philosophy and Computer Science PERVS’01 “Processes of Evolution in Real and Virtual Systems”, Kraków, Poland, January 10-11, 2002, pp. 107-112.
23. Schaefer R.; Sampling measure transformations for the genetic algorithms with heuristics (Przetwarzanie miar próbkowania dla algorytmów genetycznych z heurystyką). Proc. of the 1th Workshop on Genetic Algorithms on Szyndzielnia, Bielsko-Szyndzielnia, 26-27.04.2002, Bielska Wyższa Szkoła Biznesu i Informatyki im. J. Tyszkiewicza, Wydawnictwo Text, Kraków 2002, (in Polish), pp. 32-35.
24. Schaefer R.; The role of heuristics in serial and parallel genetic search. Abstract book of the 3rd Conf. on Numerical Analysis, Krynica, June 5-9, 2002, pp. 16–17, (invited paper).
25. Schaefer R., Kołodziej J.; Genetic search reinforced by the population hierarchy. in De Jong K. A., Poli R., Rowe J. E. (Eds.) Foundations of Genetic Algorithms 7, Morgan Kaufman Publisher 2003, pp. 383–399.
26. Grochowski M., Schaefer R., Toporkiewicz W., Uhruski P.; An Agent-based Approach to a Hard Computing System – Smart Solid. Proc. of the International Conference on Parallel Computing in Electrical Engineering (PARELEC 2002), 22-25 September 2002, Warsaw, Poland, IEEE Computer Society Press 2002, pp. 253–258.
27. Schaefer R., Adamska-Piskorz K.; Approximation of basins of attraction with mixture resolving method. Materiały Warsztatów Naukowych: Algorytmy Ewolucyjne i Optymalizacja Globalna oraz Konferencji: Systemy Rozmyte, 23-25 September 2002, Cracow, Poland, Wydawnictwa Politechniki Warszawskiej 2002, pp. 87–94.
28. Schaefer R., Adamska K., Jabłoński Z.J.; Clustering driven by the genetic sampling measure. Proc. of the AIMETH’02, 3rd Symposium on Methods of Artificial Intelligence, Gliwice, Poland, November 13-15, 2002, pp. 361–366.
29. Wierzba B., Semczuk A., Kołodziej J., Schaefer R.: Hierarchical Genetic Strategy with real number encoding. Proc. of the 6th Conf. on Evolutionary Algorithms and Global Optimization, Łagów Lubuski 2003, Wydawnictwa Politechniki Warszawskiej 2003, pp. 231–237.
30. Schaefer R.; Essential features of genetic strategies, Proc. of the CMM’03 (CD version) Pdfs/200P, CMM’03 – short papers, pp. 41–42, Wisła 2003, (invited paper).
31. Schaefer R., Adamska K.; On genetic clustering using finite mixture model – error estimation and practical tests. Proc. of the 6th Conf. on Evolutionary Algorithms and Global Optimization, Łagów Lubuski 2003, Wydawnictwa Politechniki Warszawskiej 2003, pp. 183–190.
32. Adamska K., Schaefer R., Telega H.; Genetic clustering in optimal structure design. Proc. of AIMETH’03 Symposium on Methods of Artificial Intelligence, Gliwice, Poland, November 5-7, 2003, pp. 13–16.
33. Kołodziej J., Jakubiec W., Starczak M., Schaefer R.; Identification of the CMM Parametric Errors by Hierarchical Genetic Strategy Applied. In Burczyński T., Osyczka A. (Eds.) Solid mechanics and its Applications, Vol. 117, Proc. of the IUTAM’02 Symposium on Evolutionary Methods in Mechanics, 24-27 September 2002, Cracow, Poland, Kluwer 2004, pp. 187–196.
34. Momot J., Kosacki K., Grochowski M., Uhruski P., Schaefer R.; Multi-Agent System for Irregular Parallel Genetic Computations. Lecture Notes in Computer Science, Vol. 3038, Springer 2004, pp. 623–630.
35. Schaefer R., Adamska K., Telega H.; Genetic Clustering in Continuous Landscape Exploration. Engineering Applications of Artificial Intelligence (EAAI), Vol. 17, Elsevier 2004, pp. 407–416.
36. Schaefer R., Adamska K.; Well-Tuned Genetic Algorithm and its Advantage in Detecting Basins of Attraction. Proc. of the 7th Conf. on Evolutionary Algorithms and Global Optimization, Kazimierz 24-26.05.2004, pp. 149–154.
37. Schaefer R.; Detailed evaluation of the schemata cardinality modification at the single evolution step. Proc. of the 7th Conf. on Evolutionary Algorithms and Global Optimization, Kazimierz 24-26.05.2004, pp. 143–147.
38. Kołodziej J., Schaefer R., Paszyńska A.; Hierarchical genetic computation in optimal design. Journal of Theoretical and Applied Mechanics, Vol. 42, no. 3, Warsaw 2004, pp. 78–97.
39. Paszyński M., Barabasz B., Schaefer R.; Efficient adaptive strategy for solving inverse problems. Lecture Notes in Computer Science 4487, Springer 2007, pp 342–349.
40. Schaefer R., Barabasz B., Paszyński M.; Twin adaptive scheme for solving inverse problems. Proc. of Conf. on Evolutionary Algorithms and Global Optimization KAEiOG 2007, 2007, pp. 241–249.
41. Schaefer R. (with the chapter 6 written by Telega H.); Foundation of Genetic Global Optimization, Studies in Computational Intelligence Series 74, Springer 2007.
42. Schaefer R., Barabasz B.; Asymptotic behavior of hp-HGS (hp-adaptive Finite Element Method coupled with the Hierarchic Genetic Strategy) by solving inverse problems. Proc. of ICCS’08 Conf., Part III, Kraków 23-25 June 2008, LNCS 5103, pp. 682–691.
43. Byrski A., Schaefer R.; Immunological Mechanism for Asynchronous Evolutionary Computation Boosting. Proc. of ICMAM’08 Conf., Kraków 28-31 March 2008, CD version only.
44. Schaefer R., Barabasz B., Paszyński M.; Asymptotic guarantee of success of the hp-HGS strategy. Proc. of KAEGiOG’08 Conf., Warsaw University of Technology Press, Warsaw 2008, pp. 189–196.
45. Schaefer R., Barabasz B., Paszyński M.; Solving inverse problems by the multi-deme hierarchic genetic strategy. Proc. of the 2009 IEEE Congress on Evolutionary Computations CEC’2009, Trondheim 17-21.05.2009, IEEE Catalog Number: CFP09ICE-CDR, ISBN: 978-1-4244-2959-2, Library of Congress: 2008908739.
46. Byrski A., Schaefer R.; Formal Model for Agent-Based Asynchronous Evolutionary Computation. Proc. of the 2009 IEEE Congress on Evolutionary Computations CEC’2009, Trondheim 17-21.05.2009, IEEE Catalog Number: CFP09ICE-CDR, ISBN: 978-1-4244-2959-2, Library of Congress: 2008908739.
47. Barabasz B., Schaefer R., Paszyński M.; Handling ambiguous inverse problems by the adaptive genetic strategy hp-HGS. G. Allen et al. (Eds.): ICCS 2009, Part II, LNCS 5545, Springer Verlag 2009, pp. 904–913.
48. Schaefer R., Preuss M.; Niching in Evolutionary Algorithms: From Single-Objective to Multi-Objective and Back. In: Borkowski A., Nagl M. (Eds.) Extended Abstracts, First polish-German Workshop on Resesarch Co-operation in Computer Science, PGCS’2009, Polish Academy Of Sciences Press (Division IV – Technical Sciences), Kraków, Poland, 15 June 2009, pp. 59–64.
49. Byrski A., Schaefer R.; Stochastic Dynamics of Evolutionary Multi-Agent Systems. In Arabas J. (Ed.) Evolutionary Computation and Global Optimization 2009, Oficyna Wydawnicza Politechniki Warszawskiej, Prace Naukowe, Elektronika, z. 169, Warszawa 2009, pp. 27–34.
50. Barabasz B., Schaefer R., Paszyński M., Migórski S.; Multi-deme, twin adaptive strategy hp-HGS. Waszczyszyn Z., Ziemiański Z. (Eds.) Book of Abstracts of IPM’2009 Conference, ECCOMAS Thematic Conference Series 2009, pp. 9–10.
51. Byrski A., Schaefer R.; An Attempt to Stochastic Modeling of Memetic Systems. Ruhul Sarker, Tapabrata Ray (Eds.) Agent-Based Evolutionary Search. Evolutionary Learning and Optimization Series, Vol. 5, Springer 2010, pp. 179–202.
52. Jojczyk P., Schaefer R.; Global impact balancing in the hierarchic genetic search. Computing and Informatics, Vol. 28, 2009, V 2009-Mar-3, pp. 1001–1013.
53. Byrski A., Schaefer R.; Stochastic Model of Evolutionary and Immunological Multi-Agent Systems: Mutually Excluded Actions. Fundamenta Informaticae, Vol. 95, No. 2-3, pp. 263 – 285, IOS Press 2009.
54. Schaefer R., Byrski A., Smołka M.; Stochastic Model of Evolutionary and Immunological Multi-Agent Systems: Parallel Execution of Local Actions. Fundamenta Informaticae, Vol. 95, No. 2-3, pp. 325 – 348, IOS Press 2009.
55. Schaefer R., Byrski A.; Multiagent approach to memetic computing systems. In Burczyński T., Periaux J. (Eds.) Book of abstracts, EUROGEN 2009 Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Social Problems, Cracow, Poland, June 15-17, 2009, pp. 99 – 100.
56. Schaefer R., Byrski A.; Evolutionary Multi-agent Systems: an Attempt to Asymptotic Analysis and Application to Engineering Computation. in Evolutionary and Deterministic Methods for Design, Optimization and Control. Application to Industrial and Social Problems, T. Burczyński and J. Périaux (Eds.), CIMNE, A series of Handbooks on Theory and Engineering Applications of Computational Methods, Barcelona, Spain 2011, pp. 34–43.
57. Barabasz B., Migórski S., Schaefer R., Paszyński M.; Multi-deme, twin adaptive strategy hp-HGS. Inverse Problems in Science and Engineering, Volume 19, Issue 1, January 2011, pp. 3–16.
58. Byrski A., Schaefer R., Smołka M., Cotta C.; Asymptotic Analysis of Computational Multi-Agent Systems. Proceedings of 11th International Conference on Parallel Problem Solving from Nature – PPSN XI, LNCS 6238, pp. 475–484, Springer Verlag 2010.
59. Gajda E., Schaefer R., Smołka M.; Evolutionary Multiobjective Optimization Algorithm as a Markov System. Proceedings of 11th International Conference on Parallel Problem Solving from Nature – PPSN XI, LNCS 6238, pp. 617–626, Springer Verlag 2010.
60. Barabasz B., Gajda E., Migórski S., Paszyński M., Schaefer R.; Studying inverse problems in elasticity by hierarchic genetic search. Waszczyszyn Z., Ziemiański Z. (Eds.) IPM’2011 Conference Proceedings, ECCOMAS Thematic Conference Series 2011, pp. 9–10.
61. Wolny A., Schaefer R.; Improving Population-Based Algorithms with Fitness Deterioration. Journal of Telecommunications and Information Technology, no. 4, 2011 (MS 70011), pp. 31–44.
62. Byrski A., Schaefer R., Smołka M.; Asymptotic features of parallel agent-based immunological system. In eds. Tadeusz Burczyński, Joanna Kołodziej, Aleksander Byrski, Marco Carvalho. Proc. of 25th European Conference on Modelling and Simulation : June 7–10, 2011, Kraków, Poland, pp. 518–524.
63. Barabasz B., Gajda E., Pardo D., Paszyński M., Schaefer R., Szeliga D.; hp-HGS twin adaptive strategy for inverse resistivity logging measurements. In Borkowski A., Lewinski T., Dzierzanowski G. eds. Proc. of 19th international conference on Computer Methods in Mechanics CMM 2011, 9–12 May 2011, Warsaw, Poland, pp. 121–122.
64. Byrski A., Schaefer R., Smołka M.; Markov Chain Based Analysis of Agent-Based Immunological System. N.T. Nguyen (Ed.) Transactions on Computational Collective Intelligence X Series, LNCS, Vol. 7776, Springer Verlag, 2013, pp. 1–15.
65. Schaefer R., Byrski A., Kołodziej J., Smołka M.; An agent-based model of hierarchic genetic search. Computers & Mathematics with Applications (CAMWA) journal, Volume 64, Issue 12, December 2012, Elsevier, pp. 3763–3776.
66. Paszyński M., Gajda-Zagórska E., Schaefer R.; hp-HGS twin adaptive strategy for inverse DC/AC resistivity logging measurement simulations. 10th World congress on computational mechanics. 8–13 July 2012, São Paulo, Brazil: Book of abstracts. ISBN 978-85-86686-69-6, pp. 15–16.
67. Schaefer R., Byrski A., Smołka M.; Island Model as Markov Dynamic System. International Journal of Applied Mathematics & Computer Science, Vol. 22, No. 4, 2012, pp. 971–984.
68. Gajda-Zagórska E., Paszyński M., Schaefer R., Pardo D., Calo V.; hp-HGS strategy for inverse 3D DC resistivity logging measurement simulations. International Conference on Computational Science 2012, Procedia Computer Science, Vol. 9, Elsevier 2012, pp. 927–936, DOI: 10.1016/j.procs.2012.04.099.
69. Byrski A., Schaefer R., Smołka M., Cotta C.; Asymptotic Guarantee of Success for Multi-Agent Memetic Systems. Bulletin of the Polish Academy of Sciences: Technical Sciences, 61(1), 2013, pp. 257–278, DOI: 10.2478/bpasts-2013-0025.
70. Byrski A., Schaefer R.; Markov Chain Analysis of Agent-based Evolutionary Computing in Dynamic Optimization. Proceedings of ICCS 2013, Procedia Computer Science 18 (2013), Elsevier, pp. 1475–1487, DOI: 10.1016/j.procs.2013.05.315.
71. Schaefer R., Smołka M., Paszyński M., Gajda-Zagórska E., Faliszewski P.; Essential features of inverse solvers inspired by nature. Proceedings of IPM 2013, Rzeszów University of Technology Press, Rzeszów 2013, pp. 55–56.
72. Schaefer R., Smołka M., Gajda-Zagórska E., Paszyński M., Pardo D.; Solving Inverse Problems Using Computing Agents: An Attempt to a Dedicated Hierarchic Memetic Strategy. Proceedings of IPM 2013, Rzeszów University of Technology Press, Rzeszów 2013, pp. 53–54.
73. Gajda-Zagórska E., Schaefer R.; Multiobjective hierarchic strategy for solving inverse problems. Proceedings of IPM 2013, Rzeszów University of Technology Press, Rzeszów 2013, pp. 17–18.
74. Gajda-Zagórska E., Schaefer R., Smołka M., Paszyński M., Pardo D.; Inversion of Resistivity Logging Measurements Using a Hierarchic genetic Strategy. Proceedings of IPM 2013, Rzeszów University of Technology Press, Rzeszów 2013, pp. 19–20.
75. Paszyński M., Gajda-Zagórska E., Schaefer R., Pardo D.; Hybrid algorithm for inverse DC/AC resistivity logging measurement simulations. Proceedings of 5th Asia Pacific Congress on Computational Mechanics & 4th International Symposium on Computational Mechanics APCOM&ISCM 2013, Singapore, December 11-14, 2013, http://www.sc1-en-tech.com/apcom2013/APCOM2013-Proceedings/PDF_FullPaper/1161.pdf
76. Paszyński M., Gajda-Zagórska E., Schaefer R., Pardo D.; hp-HGS strategy for inverse AC/DC resistivity logging measurement simulations. Computer Science, 14(4), 2013, pp. 629-644, DOI: dx.doi.org/10.7494/csci.2013.14.4.629.
77. Barabasz B., Gajda-Zagórska E., Migórski S., Paszyński M., Schaefer R., Smołka M.; A hybrid algorithm for solving inverse problems in elasticity. International Journal of Applied Mathematics and Computer Science, Vol. 24, No. 4, 2014, pp. 865–886, DOI: 10.2478/amcs-2014-0064.
78. Smołka M., Schaefer R.; A Memetic Framework for Solving Difficult Inverse Problems, In: Anna I. Esparcia-Alcázar, Antonio M. Mora (Eds.) Applications of Evolutionary Computation. 17th European Conference, EvoApplications 2014, Granada, Spain, April 23-25, 2014, Revised Selected Papers, LNCS, Vol. 8602, Springer, pp. 138–149.
79. Gajda-Zagórska E., Schaefer R., Smołka M., Paszyński M., Pardo D.; A hybrid method for inversion of 3D DC logging measurements, Natural Computing, Volume 14, Issue 3, Springer 2015, pp. 355-374, DOI:10.1007/s11047-014-9440-y.
80. Smołka M., Schaefer R., Paszyński M., Pardo D., Álvarez-Aramberri J.; Agent-oriented hierarchic strategy for solving inverse problems. International Journal of Applied Mathematics and Computer Science 2015, Vol. 25, No. 3, pp. 483–498, DOI:10.1515/amcs-2015-0036.
81. Obuchowicz A.K, Smołka M., Schaefer R.; Hierarchic Genetic Search with α-Stable Mutation, Proc. of Evo 2015, Copenhagen, April 8-10, 2015, Lecture Notes in Computer Science, Vol. 9028, pp. 143–154, DOI: 10.1007/978-3-319-16549-3_12.
82. Smołka M., Gajda-Zagórska E., Schaefer R., Paszyński M., Pardo D.; A hybrid method for inversion of 3D AC logging measurements, Applied Soft Computing, 2015, Vol. 36, pp. 422–456, DOI: 10.1016/j.asoc.2015.06.055.
83. Gajda-Zagórska E., Smołka M., Schaefer R., Pardo D., Álvarez-Aramberri J.; Multi-objective Hierarchic Memetic Solver for Inverse Parametric Problems. Procedia Computer Science, Vol. 51, Elsevier, ICCS 2015, pp. 974–983, DOI:10.1016/ j.procs.2015.05.239.
84. Faliszewski P., Smołka M., Schaefer R., Paszyński M; On the Computational Cost and Complexity of Stochastic Inverse Solvers. Computer Science, 17 (2) 2016, pp. 225-264, DOI: http://dx.doi.org/10.7494/csci.2016.17.2.225.
85. Faliszewski P., Sawicki J., Schaefer R., Smołka M.; Multiwinner Voting in Genetic Algorithms for Solving Ill-Posed Global Optimization Problems. Lecture Notes in Computer Science, vol. 9597, pp. 409-424, Springer 2016.
86. Faliszewski P., Sawicki J., Schaefer R., Smołka M.; Multiwinner Voting in Genetic Algorithms. IEEE Intelligent System, Volume 32, Issue 1, pp. 40-48, IEEE Computer Society 2017, DOI: 10.1109/MIS.2017.5.
87. Gajda-Zagórska E., Schaefer R., Smołka M., Pardo D., Álvarez-Aramberri J.; A Multi-objective Memetic Inverse Solver Reinforced by Local Optimization Methods. Journal of Computational Science, Volume 18, January 2017, pp. 85 – 94, Elsevier, DOI: 10.1016/j.jocs.2016.06.007.
88. Sawicki J., Smołka M., Łoś M., Schaefer R., Faliszewski P.; Two-phase strategy managing insensitivity in global optimization. In: G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I,, Lecture Notes in Computer Science, Volume 10199, Springer 2017, pp. 266-281, DOI: 10.1007/978-3-319-55849-3_18.
89. Łoś M., Schaefer R., Sawicki J., Smołka M.; Local Misfit Approximation in Memetic Solving of Ill-posed Inverse Problems. In: G. Squillero and K. Sim (Eds.): EvoApplications 2017, Part I,, Lecture Notes in Computer Science, Volume 10199, Springer 2017, pp. 297-309, DOI: 10.1007/978-3-319-55849-3_20.
90. Łoś M., Sawicki J., Smołka M., Schaefer R.; Memetic approach for irremediable ill-conditioned parametric inverse problems. (ICCS 2017), Procedia Computer Science, 108C (2017), pp. 867-876, Elsevier 2017, DOI: 10.1016/j.procs.2017.05.007
91. Smołka M., Schaefer R., Pardo D., Álvarez-Aramberri J.; Local Tikhonov regularization in the hierarchic memetic inverse solver. Proc. of ECCOMAS Int. Conf. IPM 2017 on Inverse Problems in Mechanics of Structure and Materials, Rzeszów University of Technology Press, pp. 55-56.
92. Jakub Sawicki, Marcin Łoś, Maciej Smołka, Robert Schaefer, Julen Álvarez-Aramberri; Approximating landscape insensitivity regions in solving ill-conditioned inverse problems. Memetic Computing, Vol. 10, pp. 279 – 289, Springer 2018, DOI: https://doi.org/10.1007/s12293-018-0258-5
93. Marcin Łoś, Maciej Smołka, Robert Schaefer, Jakub Sawicki,; Misfit landforms imposed by ill-conditioned inverse parametric problems. Computer Science, Vol. 19, Issue 2, 2018, AGH Press, pp: 157-178, DOI: https://doi.org/10.7494/csci.2018.19.2.2781
94. Jakub Sawicki, Maciej Smołka, Marcin Łoś, Robert Schaefer; Approximation of the objective insensitivity regions using Hierarchic Memetic Strategy coupled with Covariance Matrix Adaptation Evolutionary Strategy. The International Conference on Optimization and Learning, Bangkok, Thailand, January 29-31, 2019. arXiv: http://arxiv.org/abs/1905.07288
95. Jakub Sawicki, Maciej Smołka, Marcin Łoś, Robert Schaefer; Handling insensitivity in multi-physics inverse problems using a complex evolutionary strategy. Submitted to: Computer Methods in Material Science.

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