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Memetic Computing

, Volume 7, Issue 3, pp 159–180 | Cite as

Memes as building blocks: a case study on evolutionary optimization + transfer learning for routing problems

  • Liang Feng
  • Yew-Soon Ong
  • Ah-Hwee Tan
  • Ivor W. Tsang
Regular Research Paper

Abstract

A significantly under-explored area of evolutionary optimization in the literature is the study of optimization methodologies that can evolve along with the problems solved. Particularly, present evolutionary optimization approaches generally start their search from scratch or the ground-zero state of knowledge, independent of how similar the given new problem of interest is to those optimized previously. There has thus been the apparent lack of automated knowledge transfers and reuse across problems. Taking this cue, this paper presents a Memetic Computational Paradigm based on Evolutionary Optimization \(+\) Transfer Learning for search, one that models how human solves problems, and embarks on a study towards intelligent evolutionary optimization of problems through the transfers of structured knowledge in the form of memes as building blocks learned from previous problem-solving experiences, to enhance future evolutionary searches. The proposed approach is composed of four culture-inspired operators, namely, Learning, Selection, Variation and Imitation. The role of the learning operator is to mine for latent knowledge buried in past experiences of problem-solving. The learning task is modelled as a mapping between past problem instances solved and the respective optimized solution by maximizing their statistical dependence. The selection operator serves to identify the high quality knowledge that shall replicate and transmit to future search, while the variation operator injects new innovations into the learned knowledge. The imitation operator, on the other hand, models the assimilation of innovated knowledge into the search. Studies on two separate established NP-hard problem domains and a realistic package collection/deliver problem are conducted to assess and validate the benefits of the proposed new memetic computation paradigm.

Keywords

Memetic computation Evolutionary optimization of problems Learning from past experiences Culture-inspired  Evolutionary learning Transfer learning 

Notes

Acknowledgments

This work is partially supported under the A*Star-TSRP funding, by the Singapore Institute of Manufacturing Technology-Nanyang Technological University (SIMTech-NTU) Joint Laboratory and Collaborative research Programme on Complex Systems, and the Computational Intelligence Graduate Laboratory at NTU.

References

  1. 1.
    Bransford JD, Brown AL, Cocking RR (2000) How people learn: brain, mind, experience, and school. National Academies Press, WashingtonGoogle Scholar
  2. 2.
    Byrnes JP (1996) Cognitive development and learning in instructional contexts. Allyn and Bacon, BostonGoogle Scholar
  3. 3.
    Reif M, Shafait F, Dengel A (2012) Meta-learning for evolutionary parameter optimization of classifiers. Mach Learn 87(3):357–380MathSciNetGoogle Scholar
  4. 4.
    Ishibuchi H, Kwon K, Tanaka H (1995) A learning algorithm of fuzzy neural networks with triangular fuzzy weights. Fuzzy Sets Syst 71(3):277–293Google Scholar
  5. 5.
    Tan KC, Chen YJ, Tan KK, Lee TH (2005) Task-oriented developmental learning for humanoid robots. IEEE Trans Ind Electron 52(3):906–914Google Scholar
  6. 6.
    Tan KC, Liu DK, Chen YJ, Wang LF (2005) Intelligent sensor fusion and learning for autonomous robot navigation. Appl Artif Intell 19(5):433–456Google Scholar
  7. 7.
    Nojima Y, Ishibuchi H (2009) Incorporation of user preference into multi-objective genetic fuzzy rule selection for pattern classification problems. Artif Life Robot 14(3):418–421Google Scholar
  8. 8.
    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Des Eng 22(10):1345–1359Google Scholar
  9. 9.
    Taylor ME, Stone P (2009) Transfer learning for reinforcement learning domains: a survey. J Mach Learn Res 10:1633–1685Google Scholar
  10. 10.
    Pelikan M, Hauschild MW, Lanzi PL (2012) Transfer learning, soft distance-based bias, and the hierarchical boa. In: Proceedings of the 12th international conference on parallel problem solving from nature—volume part I, PPSN’12, pp 173–183Google Scholar
  11. 11.
    Bahceci E, Miikkulainen R (2008) Transfer of evolved pattern-based heuristics in games. In: IEEE symposium on computational intelligence and games, 2008. CIG ’08, pp 220–227Google Scholar
  12. 12.
    Asta S, Ozcan E, Parkes AJ, Etaner-Uyar AS (2013) Generalizing hyper-heuristics via apprenticeship learning. In: Middendorf M, Blum C (eds) EvoCOP, volume 7832 of Lecture Notes in Computer Science. Springer, Berlin, pp 169–178Google Scholar
  13. 13.
    Iqbal M, Browne W, Zhang MJ (2014) Reusing building blocks of extracted knowledge to solve complex, large-scale boolean problems. IEEE Trans Evol Comput 18:465–580Google Scholar
  14. 14.
    Jin YC (2010) Knowledge incorporation in evolutionary computation. Studies in fuzziness and soft computing. Springer, BerlinGoogle Scholar
  15. 15.
    Wang H, Kwong S, Jin YC, Wei W, Man K (2005) Agent-based evolutionary approach to interpretable rule-based knowledge extraction. IEEE Trans Syst Man Cybern C 29(2):143–155Google Scholar
  16. 16.
    Tang K, Mei Y, Yao X (2009) Memetic algorithm with extended neighborhood search for capacitated arc routing problems. IEEE Trans Evol Comput 13(5):1159–1166Google Scholar
  17. 17.
    Calian D, Bacardit J (2013) Integrating memetic search into the biohel evolutionary learning system for large-scale datasets. Memetic Comput 5(2):95–130Google Scholar
  18. 18.
    Tang LX, Wang XP (2013) A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems. IEEE Trans Evol Comput 17(1):20–45Google Scholar
  19. 19.
    Tayarani-N MH, Prugel-Bennett A (2013) On the landscape of combinatorial optimization problems. IEEE Trans Evol Comput 18(3):420–434Google Scholar
  20. 20.
    Cheng R, Zhang X, Tian Y, Jin Y (2014) An efficient approach to nondominated sorting for evolutionary multiobjective optimization. IEEE Trans Evol Comput 19:201–213Google Scholar
  21. 21.
    Sutcliffe AG, Wang D (2014) Memetic evolution in the development of proto-language. Memetic Comput 6(1):3–18Google Scholar
  22. 22.
    Ray T, Asafuddoula M, Sarker R (2014) A decomposition-based evolutionary algorithm for many objective optimization. IEEE Trans Evol Comput 19(3):445–460Google Scholar
  23. 23.
    Patvardhan C, Bansal S, Srivastav A (2015) Quantum-inspired evolutionary algorithm for difficult knapsack problems. Memetic Comput 7(2):135–155Google Scholar
  24. 24.
    Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110Google Scholar
  25. 25.
    Nguyen QC, Ong YS, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3):604–623Google Scholar
  26. 26.
    Gupta A, Ong YS, Feng L (2015) Multifactorial evolution: towards evolutionary multitasking. IEEE Trans Evol Comput (accepted)Google Scholar
  27. 27.
    Neri F, Cotta C, Moscato P (2011) Handbook of memetic algorithms. Studies in computational intelligence. Springer, BerlinGoogle Scholar
  28. 28.
    Kramer O (2010) Iterated local search with powell’s method: a memetic algorithm for continuous global optimization. Memetic Comput 2(1):69–83Google Scholar
  29. 29.
    Tang K, Mei Y, Yao X (2009) Memetic algorithm with extended neighborhood search for capacitated arc routing problems. IEEE Trans Evol Comput 13(5):1151–1166Google Scholar
  30. 30.
    Chen XS, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 5:591–607Google Scholar
  31. 31.
    Dawkins R (1976) The selfish gene. Oxford University Press, OxfordGoogle Scholar
  32. 32.
    Blackmore S (1999) The meme machine. Oxford University Press, OxfordGoogle Scholar
  33. 33.
    Ong YS, Lim MH, Chen XS (2010) Research frontier: memetic computation—past, present and future. IEEE Comput Intell Mag 5(2):24–36Google Scholar
  34. 34.
    Chu PC, Beasley JE (1997) A genetic algorithm for the generalised assignment problem. Comput Oper Res 24(1):17–23MathSciNetGoogle Scholar
  35. 35.
    Jensen MT (2003) Generating robust and flexible job shop schedules using genetic algorithms. IEEE Trans Evol Comput 7(3):275–288Google Scholar
  36. 36.
    Neri F, Toivanen J, Cascella GL et al (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinform 4(2):264–278Google Scholar
  37. 37.
    Elsayed S, Sarker R, Essam D (2012) Memetic multi-topology particle swarm optimizer for constrained optimization. In: IEEE congress on evolutionary computationGoogle Scholar
  38. 38.
    Louis SJ, McDonnell J (2004) Learning with case-injected genetic algorithms. IEEE Trans Evol Comput 8(4):316–328Google Scholar
  39. 39.
    Cunningham P, Smyth B (1997) Case-based reasoning in scheduling:reusing solution components. Int J Prod Res 35(4):2947–2961Google Scholar
  40. 40.
    Yang SX, Yao X (2008) Population-based incremental learning with associative memory for dynamic environments. IEEE Trans Evol Comput 12(5):542–561Google Scholar
  41. 41.
    Pelikan M, Hauschild MW (2012) Learn from the past: improving model-directed optimization by transfer learning based on distance-based bias. Missouri Estimation of Distribution Algorithms Laboratory, University of Missouri in St. Louis, MO, USA. Tech. Rep, 2012007Google Scholar
  42. 42.
    Santana R, Mendiburu A, Lozano JA (2012) Structural transfer using edas: An application to multi-marker tagging snp selection. In: 2012 IEEE congress on evolutionary computation (CEC), pp 1–8Google Scholar
  43. 43.
    Santana R, Armañanzas R, Bielza C, Larrañaga P (2013) Network measures for information extraction in evolutionary algorithms. International Journal of Computational Intelligence Systems 6(6):1163–1188Google Scholar
  44. 44.
    Lynch A (1991) Thought contagion as abstract evolution. J Ideas 2:3–10Google Scholar
  45. 45.
    Brodie R (1996) Virus of the mind: the new science of the meme. Integral Press, SeattleGoogle Scholar
  46. 46.
    Grant G (1990) Memetic lexicon. In: Principia Cybernetica WebGoogle Scholar
  47. 47.
    Situngkir H (2004) On selfish memes: culture as complex adaptive system. J Soc Complex 2(1):20–32Google Scholar
  48. 48.
    Heylighen F, Chielens K (2008) Cultural evolution and memetics. In: Meyers B (ed) Encyclopedia of complexity and system science. Springer, BerlinGoogle Scholar
  49. 49.
    Nguyen QH, Ong YS, Lim MH (2008) Non-genetic transmission of memes by diffusion. In: Proceedings of the 10th annual conference on genetic and evolutionary computation (GECCO ’08), (8):1017–1024Google Scholar
  50. 50.
    Meuth R, Lim MH, Ong YS, Wunsch D (2009) A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comput 1:85–100Google Scholar
  51. 51.
    Feng L, Ong Y-S, Lim M-H, Tsang IW (2014) Memetic search with inter-domain learning: a realization between cvrp and car. IEEE Trans Evol Comput. doi: 10.1109/TEVC.2014.2362558
  52. 52.
    Minsky M (1986) The society of mind. Simon & Schuster, New YorkGoogle Scholar
  53. 53.
    Dantzig G, Ramser JH (1959) The truck dispatching problem. Manag Sci 6:80–91MathSciNetGoogle Scholar
  54. 54.
    Golden B, Wong R (1981) Capacitated arc routing problems. Networks 11(3):305–315MathSciNetGoogle Scholar
  55. 55.
    Chen XS, Ong YS, Lim MH, Yeo SP (2011) Cooperating memes for vehicle routing problems. Int J Innov Comput 7(11):1–10Google Scholar
  56. 56.
    Cordeau JF, Laporte G, Mercier A (2001) A unified tabu search heuristic for vehicle routing problems with time windows. J Oper Res Soc 52:928–936Google Scholar
  57. 57.
    Prins C (2004) A simple and effective evolutionary algorithm for the vehicle routing problem. Comput Oper Res 31:1985–2002MathSciNetGoogle Scholar
  58. 58.
    Reimann M, Doerner K, Hartl RF (2004) D-ants: savings based ants divide and conquer the vehicle routing problem. Comput Oper Res 31:563–591Google Scholar
  59. 59.
    Lin SW, Lee ZJ, Ying KC, Lee CY (2009) Applying hybrid meta-heuristics for capacitated vehicle routing problem. Expert Syst Appl 36(2, Part 1):1505–1512Google Scholar
  60. 60.
    Lacomme P, Prins C, Ramdane-Chérif W (2004) Competitive memetic algorithms for arc routing problem. Ann Oper Res 141(1–4):159–185Google Scholar
  61. 61.
    Mei Y, Tang K, Yao X (2009) Improved memetic algorithm for capacitated arc routing problem. In: IEEE congress on evolutionary computation, pp 1699–1706Google Scholar
  62. 62.
    Feng L, Ong YS, Nguyen QH, Tan AH (2010) Towards probabilistic memetic algorithm: an initial study on capacitated arc routing problem. In: IEEE congress on evolutionary computation, pp 18–23Google Scholar
  63. 63.
    Gretton A, Bousquet O, Smola A, Schölkopf B (2005) Measuring statistical dependence with hilbert-schmidt norms. In: Proceedings algorithmic learning theory, pp 63–77Google Scholar
  64. 64.
    Zhuang J, Tsang I, Hoi SCH (2011) A family of simple non-parametric kernel learning algorithms. J Mach Learn Res (JMLR) 12:1313–1347MathSciNetGoogle Scholar
  65. 65.
    Song L, Smola A, Gretton A, Borgwardt KM (2007) A dependence maximization view of clustering. In: Proceedings of the 24th international conference on machine learning, pp 815–822Google Scholar
  66. 66.
    Runco MA, Pritzker S (1999) Encyclopedia of creativity. Academic Press, LondonGoogle Scholar
  67. 67.
    Chen XS, Ong YS (2012) A conceptual modeling of meme complexes in stochastic search. IEEE Trans Syst Man Cybern C Appl Rev 99:1–8Google Scholar
  68. 68.
    Dijkstra EW (1959) A note on two problems in connection with graphs. Numerische Mathematik 1:269–271MathSciNetGoogle Scholar
  69. 69.
    Borg I, Groenen PJF (2005) Modern multidimensional scaling: theory and applications. Springer, BerlinGoogle Scholar
  70. 70.
    Chen X, Feng L, Ong YS (2012) A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem. Int J Syst Serv 43(7):1347–1366MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Liang Feng
    • 1
  • Yew-Soon Ong
    • 2
  • Ah-Hwee Tan
    • 2
  • Ivor W. Tsang
    • 3
  1. 1.College of Computer ScienceChongqing UniversityChongqingChina
  2. 2.Center for Computational Intelligence, School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Centre for Quantum Computation and Intelligent SystemsUniversity of TechnologySydneyAustralia

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