Module also offered within study programmes:
General information:
Name:
Stochastic algorithms – applications and analysis
Code:
UBPJO-114
Profile of education:
Academic (A)
Lecture language:
English
Semester:
Fall
Responsible teacher:
prof. dr hab. inż. Schaefer Robert (schaefer@agh.edu.pl)
Academic teachers:
prof. dr hab. inż. Schaefer Robert (schaefer@agh.edu.pl)
Module summary

The effective stochastic strategies searching huge date sets are presented. Special emphasis was devoted to population based algorithms and their formal and simulation verification.

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Social competence
M_K001 Student understand the necessity of continuous studying and learning algorithms, methods and computer systems implementing complex stochastic searches and their applications. Activity during classes
Skills
M_U001 Student is able to perform the individual literature study in area of stochastic searches and their applications. Case study
M_U002 Student can apply the archived knowledge for designing and implementing stochastic search systems. Case study
M_U003 Student can utilize the modern computer environment and libraries for implementing and running parallel and distributed stochastic systems. Case study
Knowledge
M_W001 Student possess the knowledge in area of basic mechanisms of stochastic searches, their algorithmic formulation and formal analysis (guarantee of success and convergence). Activity during classes
M_W002 Student possess the knowledge in area of stochastic search applications in technology and geophysics. Activity during classes
M_W003 Student possess the knowledge of complexity analysis, memory analysis and the stochastic correctness of a stochastic searches. Activity during classes
M_W004 Student possess the specific knowledge in area of designing parallel and distributed software implementing complex stochastic strategies. Activity during classes
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Others
E-learning
Social competence
M_K001 Student understand the necessity of continuous studying and learning algorithms, methods and computer systems implementing complex stochastic searches and their applications. + - - - - - - - - - -
Skills
M_U001 Student is able to perform the individual literature study in area of stochastic searches and their applications. - - + - - - - - - - -
M_U002 Student can apply the archived knowledge for designing and implementing stochastic search systems. - - + - - - - - - - -
M_U003 Student can utilize the modern computer environment and libraries for implementing and running parallel and distributed stochastic systems. - - + - - - - - - - -
Knowledge
M_W001 Student possess the knowledge in area of basic mechanisms of stochastic searches, their algorithmic formulation and formal analysis (guarantee of success and convergence). + - - - - - - - - - -
M_W002 Student possess the knowledge in area of stochastic search applications in technology and geophysics. + - - - - - - - - - -
M_W003 Student possess the knowledge of complexity analysis, memory analysis and the stochastic correctness of a stochastic searches. + - - - - - - - - - -
M_W004 Student possess the specific knowledge in area of designing parallel and distributed software implementing complex stochastic strategies. + - - - - - - - - - -
Module content
Lectures:
Stochastic algorithms – applications and analysis

1. Random variables, stochastic process with a continuous and discrete space of states, stochastic chain, Markov and shift conditions, ergodicity, Prochorov theorem.
2. Monte Carlo methods and Pure Random Walk and Pure Random Search.
3. Asymptotic guarantee of success and stochastic asymptotic correctness.
4. Computational and memory complexity of stochastic searches. Simplest definition of stochastic convergence. Approximation of the first hitting time.
5. Formal analysis of Monte Carlo methods by the first hitting time evaluation.
6. Rigorous formulation of global optimization problems. Important examples: Analyzing huge data repositories, minimizing L-J potential for complicated particles, inverse problems in optimal design, defectoscopy and oil investigation.
7. Other stochastic algorithms: Simulated Annealing, Tabu Search, etc.
8. Genetic algorithms with a finite genetic universum. Space of states and the Markov model of dynamics.
9. Genetic algorithms with heuristic. SGA cese. Vose asymptotic theorems and application of the Prochorov theorem.
10. Algorithms with a continuous search space.
11. Multi-deme searches: Island Model, Hierarchic Genetic Strategy, memetic strategies.
12. Adaptive searches. Balancing exploration and exploitation. Adaptation of the search accuracy.
13. Two-phase strategies. Clustered genetic search.
14. Stochastic polioptimization, EMOA strategies.
15. Stochastic methods of solving inverse problems.

Laboratory classes:
Stochastic algorithms – applications and analysis

1.Introduction into selection and application of random generators. Designing and running uniform distribution generators. Gauss and Cauchy distribution generators in one and many dimensions. Generation of the alpha-stable random variables. Generation of sets with a large discrepancy.
2. Methodology of testing stochastic searches. Finite budget and finite accuracy programs.
3. Preparing materials for the project. Bibliographical analysis of the selected group of algorithms.
4. Algorithmic inventions in area of selected group of algorithms.
5. Benchmark selection and test program. Parameter tuning.
6. Working out test results. Deriving qualitative conclusions.
7. Project assessment and defense.

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 166 h
Module ECTS credits 6 ECTS
Preparation for classes 60 h
Completion of practical placements 40 h
Contact hours 56 h
Examination or Final test 10 h
Additional information
Method of calculating the final grade:

Student has to get a positive lab assessment and a positive test result which checks the knowledge tought during the lectures.

Final assessment will based on the average of test and lab assessments – sr.
The final mark OK will be determined by the following rule:

if sr > 4.75 then OK := 5.0 else {if sr > 4.25 then OK := 4.5 else {if sr > 3.75 then OK := 4.0 else {if sr > 3.25 then OK := 3.5 else OK := 3}}}
Prerequisites and additional requirements:

Mathematical analysis, Algebra, Mathematical statistics

Recommended literature and teaching resources:

1)Billingsley P.; Probability and Measure. John Willey and Sons, New York, Chichaster, Brisbane, Toronto, 1979.
2) Sahoo P.; Probability and mathematical statistics. University of Luisville, KY 40292 USA. http://www.math.louisville.edu/~pksaho01/teaching/Math662TB-09S.pdf
3)Birattari M.; Tuning Metaheuristics. Springer 2009.
4)Neri F., Cotta C., Moscato P. (eds.); Handbook of Memetic Algorithms, Springer 2012.
5)Panos M. Pardalos, H. Edwin Romeijn; Handbook of Global Optimization, Volume 2 (Nonconvex Optimization and its Applications), Kluver Academic Publisher 2002.
6)Schaefer R., (with the chapter 6 written by Telega H.), Foundation of Global Genetic Optimization. Springer 2007.

Scientific publications of module course instructors related to the topic of the module:

1. 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.
2. 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.
3. Schaefer R., Onderka Z.; Markov Chain Based Management of Large Scale Distributed Computations of Earthen Dam Leakages. Proc. of the 2nd Int. Meeting on Vector and Parallel Computing VECPAR’96, Porto, Portugal, 25-27 September 1996.
4. Schaefer R., Onderka Z.; Markov Chain Based Management of Large Scale Distributed Computations of Earthen Dam Leakages. Lecture Notes in Computer Science 1215, Springer 1997, pp. 49–64.
5. Schaefer R., Cabib E.; Optimal pretraction design in network structures. Strojnicky Časopis (Mechanical Engineering), Vol. 48, No. 3, pp. 191–202, Bratislava 1997.
6. Onderka Z., Schaefer R.; Optimal Stochastic Control of the Large Distributed Application of the Master-Slave Structure. Proc. of the 2nd Conf. on Evolutionary Algorithms and Global Optimization, Rytro, 15-19 September 1997, pp. 201-208.
7. Schaefer R., Foryś W., Onderka Z., Myśliwiec G., Sipowicz J.; Markov Control in computer networks. Proc. of the 2nd Conf. on Evolutionary Algorithms and Global Optimization – Tutorial, Rytro, 15-19 September 1997, pp. 41-68 Warsaw Univ. of Technology Press, Warszawa 1998 (invited paper).
8. Schaefer R., Flasiński M., Toporkiewicz W.; Optimal Stochastic Scaling of CAE parallel computations. Lecture Notes in Artificial Intelligence 1424, pp. 557-564, Springer 1998.
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. Danielewski M., Bożek B., Holly K., Myśliwiec G., Sipowicz J., Schaefer R.; Distributed simulation strategies of graphite electrode forming process. Proc. of the 3rd Int. Meeting on Vector and Parallel Computing VECPAR’98, Porto, Portugal, 21-23 June 1998.
11. 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).
12. 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.
13. 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).
14. 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.
15. 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.
16. 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).
17. 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.
18. 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.
19. Schaefer R.; Simple taxonomy of the genetic global optimization. Computer Assisted Mechanics and Engineering Sciences CAMES , Vol. 9, pp. 139-145, 2002.
20. 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).
21. 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.
22. 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.
23. 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.
24. 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.
25. 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).
26. 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.
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. Smołka M., Uhruski P., Schaefer R., Grochowski M.; The Dynamics of Computing Agent Systems. Lecture Notes in Computer Science Vol. 3516, pp. 727–734, Springer 2005.
40. Grochowski M., Smołka M., Schaefer R.; Architectural principles and scheduling strategies for computing agent systems. Fundamenta Informaticae 71, IOS Press 2006, pp. 15–26.
41. Kalita P., Schaefer R.; Dynamics of the weakly nonlinear Koiter shell, in: Shell Structures: Theory and Applications – Pietraszkiewicz & Szymczak (eds), Taylor & Francis Group/Balkema, London, 2005, pp. 125–128.
42. Smołka M., Schaefer R.; Computing MAS Dynamics Considering the Background Load. Lecture Notes in Computer Science, Vol. 3993, Springer 2006, pp. 799–806.
43. Paszyński M., Barabasz B., Schaefer R.; Efficient adaptive strategy for solving inverse problems. Lecture Notes in Computer Science 4487, Springer 2007, pp 342–349.
44. 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.
45. Bielecki A., Hajto P., Schaefer R.; Hybrid Neural Systems in Market Trends Prediction. in Brabazon A., O’Neill M. (Eds.) Natural Computing in Computational Economics and Finance, Studies in Computational Intelligence Series 100, Springer 2008, pp. 211–232.
46. Schaefer R. (with the chapter 6 written by Telega H.); Foundation of Genetic Global Optimization, Studies in Computational Intelligence Series 74, Springer 2007.
47. 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.
48. Byrski A., Schaefer R.; Immunological Mechanism for Asynchronous Evolutionary Computation Boosting. Proc. of ICMAM’08 Conf., Kraków 28-31 March 2008, CD version only.
49. 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.
50. Cetnarowicz K., Schaefer R., Zheng B., Paszyński M., Śnieżyński B.; Intelligent Agents and Evolvable Systems. Proc. of ICCS’08 Conf., Part III, Kraków 23-25 June 2008, LNCS 5103, pp. 533–534.
51. 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.
52. 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.
53. 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.
54. Schaefer R., Cetnarowicz K., Zheng B., Śnieżyński B.; Toward the new generation of intelligent distributed computing systems. G. Allen et al. (Eds.): ICCS 2009, Part II, LNCS 5545, pp. 813–814, Springer Verlag 2009.
55. 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.
56. 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.
57. 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.
58. 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.
59. 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.
60. 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.
61. 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.
62. 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.
63. 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.
64. 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.
65. 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.
66. 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.
67. 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.
68. 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.
69. 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.
70. 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.
71. 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.
72. 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.
73. 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.
74. 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.
75. 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.
76. 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.
77. 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.
78. 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.
79. 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.
80. 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.
81. 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.
82. 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
83. 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.
84. 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.
85. 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.
86. 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.
87. 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.
88. 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.
89. 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.
90. 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.
91. Schaefer R., Smołka M., Dalcin M., Paszyński M.; A new time integration scheme for Cahn-Hilliard equations. Procedia Computer Science, Vol. 51, Elsevier, ICCS 2015, pp. 1003–1012, DOI: 10.1016/j.procs.2015.05.244.
92. 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.
93. 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.
94. 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.
95. Faliszewski P., Laslier J.F., Scheafer R., Skowron P., Slinko A., Talmon N.; Modeling Representation of Minorities Under Multiwinner Voting Rules. Submitted to SSC 2016 Conference.
96. Cotta C., Schaefer R.; Complex Metaheuristics. Journal of Computational Sciences. 17 Elsevier (2016) pp. 171-173, doi:10.1016/j.jocs.2016.06.001.
97. 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.
98. 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.
99. 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.
100. 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
101. 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.
102. Jakub Sawicki, Marcin Łoś, Maciej Smołka, Robert Schaefer, Julen Álvarez-Aramberri; Approximating landscape insensitivity regions in solving ill-conditioned inverse problems. Accepted to Memetic Computing.
103. Cotta C., Schaefer R.; Bioinspired Algorithms and Complex Systems. Journal of Computational Science, Vol. 23, Elsevier 2017, pp. 192-194, DOI: https://doi.org/10.1016/j.jocs.2017.11.010
104. Jakub Sawicki, Maciej Smołka, Marcin Łoś, Robert Schaefer; Misfit landforms imposed by ill-conditioned inverse parametric problems. Accepted to Computer Science.

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