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Multi-memetic Mind Evolutionary Computation Algorithm Based on the Landscape Analysis

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Theory and Practice of Natural Computing (TPNC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11324))

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Abstract

This paper presents a new multi-memetic modification of the Mind Evolutionary Computation (MEC) algorithm with the incorporated landscape analysis (LA) for solving global optimization problems. The proposed landscape analysis is based on the concept of Lebesgue integral and allows one to divide objective functions into three categories. Each category suggests a usage of specific hyper-heuristics for adaptive meme selection. Software implementation of the proposed method is briefly described in the paper. Efficiency of the method was compared with the multi-memetic modification of the MEC algorithm which utilizes a simple random hyper-heuristic only, without any LA procedure. Comparative performance investigation was carried out with a use of high-dimensional benchmark functions of various classes.

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References

  1. Karpenko, A.P.: Modern algorithms of search engine optimization. Nature-inspired optimization algorithms. Moscow, Bauman MSTU Publ., 446 p. (2014). (in Russian)

    Google Scholar 

  2. Sokolov, A.P., Schetinin, V.N.: Modeling of phases adhesion in composite materials based on spring finite element with zero length. Key Eng. Mater. 780, 3–9 (2018). https://doi.org/10.4028/www.scientific.net/KEM.780.3

    Article  Google Scholar 

  3. Sokolov, A.P., Pershin, AYu.: Computer-aided design of composite materials using reversible multiscale homogenization and graph-based software engineering. Key Eng. Mater. 779, 11–18 (2018). https://doi.org/10.4028/www.scientific.net/KEM.779.11

    Article  Google Scholar 

  4. Weise, T.: Global Optimization Algorithms - Theory and Application. University of Kassel, 758 p. (2008)

    Google Scholar 

  5. Neri, F., Cotta, C., Moscato, P.: Handbook of Memetic Algorithms, 368 p. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23247-3

  6. Krasnogor N. Studies on the theory and design space of memetic algorithms. Ph.D. thesis, Faculty of Computing, Mathematics and Engineering, University of the West of England, Bristol, U.K. (2002)

    Google Scholar 

  7. Dawkins, R.: The Selfish Gene, 384 p. University Press, Oxford (1976)

    Google Scholar 

  8. Nguyen, Q.H., Ong, Y.S., Krasnogor, N.: A study on the design issues of memetic algorithm. In: IEEE Congress on Evolutionary Computation, pp. 2390–2397 (2007)

    Google Scholar 

  9. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  10. Chengyi, S., Yan, S., Wanzhen, W.: A survey of MEC: 1998–2001. In: 2002 IEEE International Conference on Systems, Man and Cybernetics IEEE SMC2002, Hammamet, Tunisia, vol. 6, pp. 445–453, 6–9 October 2002. Institute of Electrical and Electronics Engineers Inc. (2002). https://doi.org/10.1109/icsmc.2002.1175629

  11. Jie, J., Zeng, J.: Improved mind evolutionary computation for optimizations. In: Proceedings of 5th World Congress on Intelligent Control and Automation, Hang Zhou, China, pp. 2200–2204 (2004). https://doi.org/10.1109/wcica.2004.1341978

  12. Jie, J., Han, C., Zeng, J.: An extended mind evolutionary computation model for optimizations. Appl. Math. Comput. 185, 1038–1049 (2007). https://doi.org/10.1016/j.amc.2006.07.037

    Article  MATH  Google Scholar 

  13. Sakharov, M., Karpenko, A.: Performance investigation of mind evolutionary computation algorithm and some of its modifications. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds.) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2016), pp. 475–486. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33609-1_43

  14. Karpenko, A.P., Sakharov, M.K.: Multi-memetic global optimization based on MEC. Inf. Technol. 7, 23–30 (2014)

    Google Scholar 

  15. Sakharov, M.K., Karpenko, A.P., Velisevich, Y.I.: Multi-memetic mind evolutionary computation algorithm for loosely coupled systems of desktop computers. Sci. Educ. Bauman MSTU 10, 438–452 (2015). https://doi.org/10.7463/1015.0814435

    Article  Google Scholar 

  16. Heinz, B.: Measure and integration theory. In: De Gruyter Studies in Mathematics, vol. 26, 236 p. De Gruyter, Berlin (2001)

    Google Scholar 

  17. Sakharov, M., Karpenko, A.: A new way of decomposing search domain in a global optimization problem. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds.) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI 2017), pp. 398–407. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68321-8_41

  18. Hart, W., Krasnogor, N., Smith, J.E.: Memetic evolutionary algorithms. In: Hart, W.E., Smith, J.E., Krasnogor, N. (eds.) Studies in Fuzziness and Soft Computing, vol. 166, pp. 3–27. Springer, Heidelberg (2005). https://doi.org/10.1007/3-540-32363-5_1

    Chapter  Google Scholar 

  19. Krasnogor, N., Blackburne, B.P., Burke, E.K., Hirst, J.D.: Multimeme algorithms for protein structure prediction. In: Guervós, J.J.M., Adamidis, P., Beyer, H.-G., Schwefel, H.-P., Fernández-Villacañas, J.-L. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 769–778. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45712-7_74

    Chapter  Google Scholar 

  20. Mersmann, O. et al.: Exploratory landscape analysis. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 829–836. ACM (2011). https://doi.org/10.1145/2001576.2001690

  21. Agasiev, T., Karpenko, A.: The program system for Automated parameter tuning of optimization algorithms. In: Procedia Computer Science, vol. 103, pp. 347–354 (2017). https://doi.org/10.1016/j.procs.2017.01.120

  22. Gavrilina, E., Zakharov, M., Karpenko, A., Smirnova, E., Sokolov, A.: Model of integral assessment quality of training graduates of higher engineering education. CEUR Workshop Proceedings, vol. 1761, pp. 52–57 (2016)

    Google Scholar 

  23. Kerschke, P. et al.: Cell mapping techniques for exploratory landscape analysis. In: Tantar, A.A., et al. (eds.) EVOLVE–A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, pp. 115–131. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07494-8_9

  24. Flamm, C., et al.: Barrier trees of degenerate landscapes. Z. Phys. Chem. 216(2), 155 (2002)

    Article  Google Scholar 

  25. Muñoz, M.A., Kirley, M., Halgamuge, S.K.: Exploratory landscape analysis of continuous space optimization problems using information content. IEEE Trans. Evol. Comput. 19(1), 74–87 (2015). https://doi.org/10.1109/TEVC.2014.2302006

    Article  Google Scholar 

  26. Preuss, M.: Improved topological niching for real-valued global optimization. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 386–395. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29178-4_39

    Chapter  Google Scholar 

  27. Munoz, M.A., Smith-Miles, K.: Effects of function translation and dimensionality reduction on landscape analysis. In: 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1336–1342. IEEE (2015)

    Google Scholar 

  28. Sobol, I.M.: Distribution of points in a cube and approximate evaluation of integrals. USSR Comput. Math. Phys. 7, 86–112 (1967)

    Article  MathSciNet  Google Scholar 

  29. Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical report, 32 p. Nanyang Technological University, Singapore (2013)

    Google Scholar 

  30. Ong, Y.S., Lim, M.H., Meuth, R.: A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comput. 1(2), 85–100 (2009)

    Article  Google Scholar 

  31. Ong, Y.S., Lim, M.H., Zhu, N., Wong, K.W.: Classification of adaptive memetic algorithms: a comparative study. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(1), 141–152 (2006)

    Article  Google Scholar 

  32. Nelder, J.A., Meade, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)

    Article  MathSciNet  Google Scholar 

  33. Solis, F.J., Wets, R.J.-B.: Minimization by random search techniques. Math. Oper. Res. 6, 19–30 (1981)

    Article  MathSciNet  Google Scholar 

  34. Floudas, A.A., et al.: Handbook of Test Problems in Local and Global Optimization, 441 p. Kluwer, Dordrecht (1999)

    Google Scholar 

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Authors and Affiliations

  1. Bauman MSTU, Moscow, Russia

    Maxim Sakharov & Anatoly Karpenko

Authors
  1. Maxim Sakharov
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  2. Anatoly Karpenko
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Correspondence to Maxim Sakharov .

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Editors and Affiliations

  1. School of Business, University College Dublin, Dublin, Ireland

    David Fagan

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. School of Business, University College Dublin, Dublin, Ireland

    Michael O'Neill

  4. Escuela Politécnica, University of Extremadura, Caceres, Spain

    Miguel A. Vega-Rodríguez

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Sakharov, M., Karpenko, A. (2018). Multi-memetic Mind Evolutionary Computation Algorithm Based on the Landscape Analysis. In: Fagan, D., Martín-Vide, C., O'Neill, M., Vega-Rodríguez, M.A. (eds) Theory and Practice of Natural Computing. TPNC 2018. Lecture Notes in Computer Science(), vol 11324. Springer, Cham. https://doi.org/10.1007/978-3-030-04070-3_19

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  • DOI: https://doi.org/10.1007/978-3-030-04070-3_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04069-7

  • Online ISBN: 978-3-030-04070-3

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