Skip to main content
SpringerLink
Log in

Evolutionary Dynamic Optimization: Methodologies

  • Conference paper
Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

Abstract

In recent years, Evolutionary Dynamic Optimization (EDO) has attracted a lot of research effort and has become one of the most active research areas in evolutionary computation (EC) in terms of the number of activities and publications. This chapter provides a summary of main EDO approaches in solving DOPs. The strength and weakness of each approach and their suitability for different types of DOPs are discussed. Current gaps, challenging issues and future directions regarding EDO methodolgies are also presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbass, H.A., Deb, K.: Searching under multi-evolutionary pressures. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 391–404. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  2. Andersen, H.C.: An investigation into genetic algorithms, and the relationship between speciation and the tracking of optima in dynamic functions. Honours thesis, Queensland University of Technology, Brisbane, Australia (1991)

    Google Scholar 

  3. Angeline, P.J.: Tracking extrema in dynamic environments. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 335–345. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

  4. Arnold, D.V., Beyer, H.-G.: Random Dynamics Optimum Tracking with Evolution Strategies. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 3–12. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Arnold, D.V., Beyer, H.G.: Optimum tracking with evolution strategies. Evol. Comput. 14(3), 291–308 (2006)

    Article  Google Scholar 

  6. Azevedo, C., Araujo, A.: Generalized immigration schemes for dynamic evolutionary multiobjective optimization. In: Proc. 2011 IEEE Congr. Evol. Comput., pp. 2033–2040 (2011)

    Google Scholar 

  7. Bäck, T.: On the behavior of evolutionary algorithms in dynamic environments. In: Proc. 1998 IEEE Int. Conf. on Evol. Comput., pp. 446–451 (1998)

    Google Scholar 

  8. Bendtsen, C.N., Krink, T.: Dynamic memory model for non-stationary optimization. In: Proc. 2002 IEEE Congr. Evol. Comput., pp. 145–150 (2002)

    Google Scholar 

  9. Blackwell, T.: Particle swarm optimization in dynamic environment. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51, pp. 28–49. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  10. Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans. Evol. Comput. 10(4), 459–472 (2006)

    Article  Google Scholar 

  11. Blackwell, T.M., Bentley, P.J.: Dynamic search with charged swarms. In: Proc. 2002 Genetic and Evol. Comput. Conf., pp. 19–26 (2002)

    Google Scholar 

  12. Bosman, P.A.N.: Learning, anticipation and time-deception in evolutionary online dynamic optimization. In: Yang, S., Branke, J. (eds.) GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization (2005)

    Google Scholar 

  13. Bosman, P.A.N.: Learning and anticipation in online dynamic optimization. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51, pp. 129–152. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Bosman, P.A.N., Poutré, H.L.: Learning and anticipation in online dynamic optimization with evolutionary algorithms: the stochastic case. In: Proc. 2002 Genetic and Evol. Comput. Conf., pp. 1165–1172 (2007)

    Google Scholar 

  15. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proc. 1999 IEEE Congr. Evol. Comput., vol. 3, pp. 1875–1882 (1999)

    Google Scholar 

  16. Branke, J.: Evolutionary approaches to dynamic environments - updated survey. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 27–30 (2001)

    Google Scholar 

  17. Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer (2001)

    Google Scholar 

  18. Branke, J.: Evolutionary approaches to dynamic optimization problems – introduction and recent trends. In: Branke, J. (ed.) GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 2–4 (2003)

    Google Scholar 

  19. Branke, J., Kaußler, T., Schmidth, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Proc. 4th Int. Conf. Adaptive Comput. Des. Manuf., pp. 299–308 (2000)

    Google Scholar 

  20. Branke, J., Mattfeld, D.: Anticipation and flexibility in dynamic scheduling. Int. J. of Production Research 43(15), 3103–3129 (2005)

    Article  Google Scholar 

  21. Branke, J., Orbayı, M., Uyar, Ş.: The role of representations in dynamic knapsack problems. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 764–775. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Branke, J., Salihoglu, E., Uyar, Ş.: Towards an analysis of dynamic environments. In: Proc. 2005 Genetic and Evol. Comput. Conf., pp. 1433–1439 (2005)

    Google Scholar 

  23. Branke, J., Wang, W.: Theoretical analysis of simple evolution strategies in quickly changing environments. In: Proc. 2003 Genetic and Evol. Comput. Conf., pp. 537–548 (2003)

    Google Scholar 

  24. Bui, L., Abbass, H., Branke, J.: Multiobjective optimization for dynamic environments. In: Proc. 2005 IEEE Congr. Evol. Comput., vol. 3, pp. 2349–2356 (2005)

    Google Scholar 

  25. Carlisle, A., Dozier, G.: Adapting particle swarm optimisationto dynamic environments. In: Proc. 2000 Int. Conf. on Artif. Intell., pp. 429–434 (2000)

    Google Scholar 

  26. Carlisle, A., Dozier, G.: Tracking changing extrema with adaptive particle swarm optimizer. In: Proc. 5th World Automation Congr., vol. 13, pp. 265–270 (2002)

    Google Scholar 

  27. Cedeno, W., Vemuri, V.R.: On the use of niching for dynamic landscapes. In: Proc. 1997 IEEE Int. Conf. on Evol. Comput. (1997)

    Google Scholar 

  28. Cheng, H., Yang, S.: Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Eng. Appl. of Artif. Intell. 23(5), 806–819 (2010)

    Article  Google Scholar 

  29. Cheng, H., Yang, S.: Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 562–571. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  30. Chitty, D.M., Hernandez, M.L.: A hybrid ant colony optimisation technique for dynamic vehicle routing. In: Deb, K., Tari, Z. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 48–59. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  31. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuouis, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA (1990)

    Google Scholar 

  32. Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. In: Proc. 1993 Int. Conf. on Genetic Algorithms, pp. 523–530 (1993)

    Google Scholar 

  33. Collingwood, E., Corne, D., Ross, P.: Useful diversity via multiploidy. In: Proc. 1996 IEEE Int. Conf. on Evol. Comput., pp. 810–813 (1996)

    Google Scholar 

  34. Daneshyari, M., Yen, G.: Dynamic optimization using cultural based pso. In: Proc. 2011 IEEE Congr. Evol. Comput., pp. 509–516 (2011)

    Google Scholar 

  35. Deb, K., Rao N., U.B., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: A case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  36. Droste, S.: Analysis of the (1+1) ea for a dynamically changing onemax-variant. In: Proc. 2002 IEEE Congr. Evol. Comput., pp. 55–60 (2002)

    Google Scholar 

  37. Eggermont, J., Lenaerts, T., Poyhonen, S., Termier, A.: Raising the dead: Extending evolutionary algorithms with a case-based memory. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 280–290. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  38. Fernández, J.L., Arcos, J.L.: Adapting particle swarm optimization in dynamic and noisy environments. In: Proc. 2010 IEEE Congr. Evol. Comput., pp. 765–772 (2010)

    Google Scholar 

  39. de França, F.O., Von Zuben, F.J.: A dynamic artificial immune algorithm applied to challenging benchmarking problems. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 423–430 (2009)

    Google Scholar 

  40. Goh, C.K., Tan, K.C.: A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Trans. on Evol. Comput. 13(1), 103–127 (2009)

    Article  Google Scholar 

  41. Goldberg, D.E., Smith, R.E.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Proc. Int. Conf. on Genetic Algorithms, pp. 59–68 (1987)

    Google Scholar 

  42. Gouvêa Jr., M., Araújo, A.: Adaptive evolutionary algorithm based on population dynamics for dynamic environments. In: Proc. 2011 Genetic and Evol. Comput. Conf., pp. 909–916 (2011)

    Google Scholar 

  43. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proc. 2nd Int. Conf. Parallel Problem Solving from Nature, pp. 137–144 (1992)

    Google Scholar 

  44. Grefenstette, J.J.: Evolvability in dynamic fitness landscapes: A genetic algorithm approach. In: Proc. 1999 IEEE Congr. Evol. Comput., vol. 3, pp. 2031–2038 (1999)

    Google Scholar 

  45. Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proc. 2006 Genetic and Evol. Comput. Conf., pp. 1201–1208 (2006)

    Google Scholar 

  46. He, J., Yao, X.: From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms. IEEE Trans. Evol. Comput. 6(5), 495–511 (2002)

    Article  Google Scholar 

  47. He, J., Yao, X.: A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing 3(1), 21–35 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  48. Hu, X., Eberhart, R.: Adaptive particle swarm optimisation: detection and response to dynamic systems. In: Proc. 2002 IEEE Congr. Evol. Comput., pp. 1666–1670 (2002)

    Google Scholar 

  49. Jansen, T., Schellbach, U.: Theoretical analysis of a mutation-based evolutionary algorithm for a tracking problem in lattice. In: Proc. 2005 Genetic and Evol. Comput. Conf., pp. 841–848 (2005)

    Google Scholar 

  50. Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer and its adaptive variant. IEEE Trans. Syst., Man, and Cybern.-Part B: Cybern. 35, 1272–1282 (2005)

    Article  Google Scholar 

  51. Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer for noisy and dynamic environments. Genetic Programming and Evolvable Machines 7(4), 329–354 (2006)

    Article  Google Scholar 

  52. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments—a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)

    Article  Google Scholar 

  53. Jin, Y., Sendhoff, B.: Constructing dynamic optimization test problems using the multi-objective optimization concept. In: Raidl, G.R., et al. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 525–536. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  54. Kramer, G.R., Gallagher, J.C.: Improvements to the *CGA enabling online intrinsic in compact EH devices. In: Proc. 2003 NASA DoD Conf. on Evolvable Hardware, pp. 235–231 (2003)

    Google Scholar 

  55. Lepagnot, J., Nakib, A., Oulhadj, H., Siarry, P.: Brain cine mri segmentation based on a multiagent algorithm for dynamic continuous optimization. In: Proc. 2011 IEEE Congr. Evol. Comput., pp. 1695–1702 (2011)

    Google Scholar 

  56. Lewis, J., Hart, E., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 139–148. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  57. Li, C., Yang, S.: A clustering particle swarm optimizer for dynamic optimization. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 439–446 (2009)

    Google Scholar 

  58. Li, X., Branke, J., Blackwell, T.: Particle swarm with speciation and adaptation in a dynamic environment. In: Proc. 2006 Genetic and Evol. Comput. Conf., pp. 51–58 (2006)

    Google Scholar 

  59. Liu, L., Wang, D., Yang, S.: Compound particle swarm optimization in dynamic environments. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 616–625. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  60. Louis, S.J., Xu, Z.: Genetic algorithms for open shop scheduling and re-scheduling. In: Cohen, M.E., Hudson, D.L. (eds.) Proc. ISCA 11th Int. Conf. on Computers and their Applications, pp. 99–102 (1996)

    Google Scholar 

  61. Lung, R.I., Dumitrescu, D.: A new collaborative evolutionary-swarm optimization technique. In: Proc. 2007 Genetic and Evol. Comput. Conf., pp. 2817–2820 (2007)

    Google Scholar 

  62. Mavrovouniotis, M., Yang, S.: Memory-based immigrants for ant colony optimization in changing environments. In: Di Chio, C., et al. (eds.) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 324–333. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  63. Mendes, R., Mohais, A.: Dynde: a differential evolution for dynamic optimization problems. In: Proc. 2005 IEEE Congr. Evol. Comput., pp. 2808–2815 (2005)

    Google Scholar 

  64. Mori, N., Kita, H., Nishikawa, Y.: Adaptation to a changing environment by means of the feedback thermodynamical genetic algorithm. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 149–158. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  65. Morrison, R.W.: Designing Evolutionary Algorithms for Dynamic Environments. Springer, Berlin (2004) ISBN 3-540-21231-0

    Google Scholar 

  66. Moser, I.: Review - all currently known publications on approaches which solve the moving peaks problem. Tech. Rep., Swinburne University of Technology, Melbourne, Australia (2007)

    Google Scholar 

  67. Moser, I., Hendtlass, T.: A simple and efficient multi-component algorithm for solving dynamic function optimisation problems. In: Proc. 2007 IEEE Congr. Evol. Comput., pp. 252–259 (2007)

    Google Scholar 

  68. Ng, K.P., Wong, K.C.: A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proc. 6th Int. Conf. on Genetic Algorithms, pp. 159–166 (1995)

    Google Scholar 

  69. Nguyen, T.T.: Tracking optima in dynamic environments using evolutionary algorithms - rsmg report 5. Tech. Rep., School of Computer Science, University of Birmingham (2008), http://www.cs.bham.ac.uk/~txn/unpublished/reports/Report_5_Thanh.pdf

  70. Nguyen, T.T.: Continuous Dynamic Optimisation Using Evolutionary Algorithms. Ph.D. thesis, School of Computer Science, University of Birmingham (2011), http://etheses.bham.ac.uk/1296 and http://www.staff.ljmu.ac.uk/enrtngu1/theses/phd_thesis_nguyen.pdf

  71. Nguyen, T.T., Yang, S., Branke, J.: Evolutionary dynamic optimization: A survey of the state of the art. Swarm and Evol. Comput. 6, 1–24 (2012)

    Article  Google Scholar 

  72. Nguyen, T.T., Yang, Z., Bonsall, S.: Dynamic time-linkage problems - the challenges. In: IEEE RIVF Int. Conf. on Computing and Communication Technologies, Research, Innovation, and Vision for the Future, pp. 1–6 (2012)

    Google Scholar 

  73. Nguyen, T.T., Yao, X.: Benchmarking and solving dynamic constrained problems. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 690–697 (2009)

    Google Scholar 

  74. Nguyen, T.T., Yao, X.: Dynamic time-linkage problems revisited. In: Giacobini, M., et al. (eds.) EvoWorkshops 2009. LNCS, vol. 5484, pp. 735–744. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  75. Nguyen, T.T., Yao, X.: Solving dynamic constrained optimisation problems using stochastic ranking and repair methods. IEEE Trans. Evol. Comput. (2010) (submitted), http://www.staff.ljmu.ac.uk/enrtngu1/Papers/Nguyen_Yao_dRepairGA.pdf

  76. Nguyen, T.T., Yao, X.: Continuous dynamic constrained optimisation - the challenges. IEEE Trans. Evol. Comput. 16(6), 769–786 (2012)

    Article  Google Scholar 

  77. Oppacher, F., Wineberg, M.: The Shifting Balance Genetic Algorithm: Improving the GA in a Dynamic Environment. In: Proc. 1999 Genetic and Evol. Comput. Conf., vol. 1, pp. 504–510 (1999)

    Google Scholar 

  78. Parrott, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Trans. Evol. Comput. 10(4), 440–458 (2006)

    Article  Google Scholar 

  79. Ramsey, C.L., Grefenstette, J.J.: Case-based initialization of genetic algorithms. In: Proc. 5th Int. Conf. on Genetic Algorithms, pp. 84–91 (1993)

    Google Scholar 

  80. Richter, H.: Behavior of evolutionary algorithms in chaotically changing fitness landscapes. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 111–120. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  81. Richter, H.: Evolutionary optimization in spatio–temporal fitness landscapes. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 1–10. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  82. Richter, H.: Detecting change in dynamic fitness landscapes. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 1613–1620 (2009)

    Google Scholar 

  83. Richter, H.: Memory design for constrained dynamic optimization problems. In: Di Chio, C., et al. (eds.) EvoApplicatons 2010, Part I. LNCS, vol. 6024, pp. 552–561. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  84. Richter, H., Yang, S.: Memory based on abstraction for dynamic fitness functions. In: Giacobini, M., et al. (eds.) EvoWorkshops 2008. LNCS, vol. 4974, pp. 596–605. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  85. Richter, H., Yang, S.: Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Comput. 13(12), 1163–1173 (2009)

    Article  MATH  Google Scholar 

  86. Riekert, M., Malan, K.M., Engelbrecht, A.P.: Adaptive genetic programming for dynamic classification problems. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 674–681 (2009)

    Google Scholar 

  87. Rohlfshagen, P., Lehre, P.K., Yao, X.: Dynamic evolutionary optimisation: An analysis of frequency and magnitude of change. In: Proc. 2009 Genetic and Evol. Comput. Conf., pp. 1713–1720 (2009)

    Google Scholar 

  88. Rohlfshagen, P., Yao, X.: Attributes of dynamic combinatorial optimisation. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 442–451. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  89. Rohlfshagen, P., Yao, X.: On the role of modularity in evolutionary dynamic optimisation. In: Proc. 2010 IEEE Congr. Evol. Comput., pp. 3539–3546 (2010)

    Google Scholar 

  90. Rossi, C., Abderrahim, M., Díaz, J.C.: Tracking moving optima using kalman-based predictions. Evol. Comput. 16(1), 1–30 (2008)

    Article  Google Scholar 

  91. Ryan, C.: The degree of oneness. In: Proc. 1st Online Workshop on Soft Computing, pp. 43–49 (1996)

    Google Scholar 

  92. Salomon, R., Eggenberger, P.: Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) AE 1997. LNCS, vol. 1363, pp. 251–262. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  93. Simões, A., Costa, E.: Memory-based chc algorithms for the dynamic traveling salesman problem. In: Proc. 2011 Genetic and Evol. Comput. Conf., pp. 1037–1044 (2011)

    Google Scholar 

  94. Simões, A., Costa, E.: An immune system-based genetic algorithm to deal with dynamic environments: Diversity and memory. In: Pearson, D.W., Steele, N.C., Albrecht, R. (eds.) Proc. 2003 Int. Conf. on Neural Networks and Genetic Algorithms (ICANNGA 2003), pp. 168–174 (2003)

    Google Scholar 

  95. Simões, A., Costa, E.: Improving memory’s usage in evolutionary algorithms for changing environments. In: Proc. 2007 IEEE Congr. Evol. Comput., pp. 276–283 (2007)

    Google Scholar 

  96. Simões, A., Costa, E.: Evolutionary algorithms for dynamic environments: Prediction using linear regression and markov chains. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 306–315. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  97. Simões, A., Costa, E.: Improving prediction in evolutionary algorithms for dynamic environments. In: Proc. 2009 Genetic and Evol. Comput. Conf., pp. 875–882 (2009)

    Google Scholar 

  98. Singh, H.K., Isaacs, A., Nguyen, T.T., Ray, T., Yao, X.: Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 3127–3134 (2009)

    Google Scholar 

  99. Stanhope, S.A., Daida, J.M.: Genetic algorithm fitness dynamics in a changing environment. In: Proc. 1999 IEEE Congr. Evol. Comput., vol. 3, pp. 1851–1858 (1999)

    Google Scholar 

  100. Tinos, R., Yang, S.: Continuous dynamic problem generators for evolutionary algorithms. In: Proc. 2007 IEEE Congr. Evol. Comput., pp. 236–243 (2007)

    Google Scholar 

  101. Tinós, R., Yang, S.: An analysis of the XOR dynamic problem generator based on the dynamical system. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI, Part I. LNCS, vol. 6238, pp. 274–283. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  102. Toffolo, A., Benini, E.: Genetic diversity as an objective in multi-objective evolutionary algorithms. Evol. Comput. 11(2), 151–167 (2003)

    Article  Google Scholar 

  103. Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proc. 1999 IEEE Congr. Evol. Comput., vol. 3, pp. 1843–1850 (1999)

    Google Scholar 

  104. Ursem, R.K.: Multinational GA optimization techniques in dynamic environments. In: Proc. 2000 Genetic and Evol. Comput. Conf., pp. 19–26 (2000)

    Google Scholar 

  105. Ursem, R.K., Krink, T., Jensen, M.T., Michalewicz, Z.: Analysis and modeling of control tasks in dynamic systems. IEEE Trans. Evol. Comput. 6(4), 378–389 (2002)

    Article  Google Scholar 

  106. Uyar, A.S., Harmanci, A.E.: A new population based adaptive domination change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput. 9(11), 803–814 (2005)

    Article  MATH  Google Scholar 

  107. Vavak, F., Fogarty, T.C., Jukes, K.: A genetic algorithm with variable range of local search for tracking changing environments. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 376–385. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  108. Vavak, F., Jukes, K., Fogarty, T.C.: Learning the local search range for genetic optimisation in nonstationary environments. In: Proc. 1997 IEEE Int. Conf. on Evol. Comput., pp. 355–360 (1997)

    Google Scholar 

  109. Vavak, F., Jukes, K.A., Fogarty, T.C.: Performance of a genetic algorithm with variable local search range relative to frequency for the environmental changes. In: Proc. 3rd Int. Conf. on Genetic Programming, pp. 602–608 (1998)

    Google Scholar 

  110. Wang, Y., Wineberg, M.: Estimation of evolvability genetic algorithm and dynamic environments. Genetic Programming and Evolvable Machines 7(4), 355–382 (2006)

    Article  Google Scholar 

  111. Weicker, K.: An analysis of dynamic severity and population size. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 159–168. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  112. Weicker, K.: Evolutionary algorithms and dynamic optimization problems. Der Andere Verlag (2003)

    Google Scholar 

  113. Weicker, K.: Analysis of local operators applied to discrete tracking problems. Soft Comput 9(11), 778–792 (2005)

    Article  MATH  Google Scholar 

  114. Weicker, K., Weicker, N.: On evolution strategy optimization in dynamic environments. In: Proc. 1999 IEEE Congr. Evol. Comput., vol. 3, pp. 2039–2046 (1999)

    Google Scholar 

  115. Woldesenbet, Y.G., Yen, G.G.: Dynamic evolutionary algorithm with variable relocation. IEEE Trans. Evol. Comput. 13(3), 500–513 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  117. Xing, L., Rohlfshagen, P., Chen, Y., Yao, X.: A hybrid ant colony optimisation algorithm for the extended capacitated arc routing problem. IEEE Trans. Syst., Man and Cybern., Part B: Cybern. 41(4), 1110–1123 (2011)

    Article  Google Scholar 

  118. Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proc. 2005 Genetic and Evol. Comput. Conf., pp. 1115–1122 (2005)

    Google Scholar 

  119. Yang, S.: Associative memory scheme for genetic algorithms in dynamic environments. In: Rothlauf, F., et al. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 788–799. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  120. Yang, S.: A comparative study of immune system based genetic algorithms in dynamic environments. In: Proc. 2006 Genetic and Evol. Comput. Conf., pp. 1377–1384 (2006)

    Google Scholar 

  121. Yang, S.: On the design of diploid genetic algorithms for problem optimization in dynamic environments. In: Proc. 2006 IEEE Congr. Evol. Comput., pp. 1362–1369 (2006)

    Google Scholar 

  122. Yang, S.: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol. Comput. 16(3), 385–416 (2008)

    Article  Google Scholar 

  123. Yang, S., Jiang, Y., Nguyen, T.T.: Metaheuristics for dynamic combinatorial optimization problems. IMA J. of Management Mathematics (2012), doi:10.1093/imaman/DPS021

    Google Scholar 

  124. Yang, S., Jin, Y., Ong, Y.S. (eds.): Evolutionary Computation in Dynamic and Uncertain Environments. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  125. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005)

    Article  MATH  Google Scholar 

  126. Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. Evol. Comput. 12(5), 542–561 (2008)

    Article  Google Scholar 

  127. Yu, E.L., Suganthan, P.N.: Evolutionary programming with ensemble of explicit memories for dynamic optimization. In: Proc. 2009 IEEE Congr. Evol. Comput., pp. 431–438 (2009)

    Google Scholar 

  128. Zeng, S., Shi, H., Kang, L., Ding, L.: Orthogonal dynamic hill climbing algorithm: ODHC. In: Yang, S., Ong, Y.S., Jin, Y. (eds.) Evolutionary Computation in Dynamic and Uncertain Environments. SCI, vol. 51, pp. 79–105. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  129. Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 832–846. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  130. Zou, X., Wang, M., Zhou, A., Mckay, B.: Evolutionary optimization based on chaotic sequence in dynamic environments. In: Proc. 2004 IEEE Int. Conf. on Networking, Sensing and Control, vol. 2, pp. 1364–1369 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Engineering, Technology and Maritime Operations, Liverpool John Moores University, Liverpool, L3 3AF, U.K.

    Trung Thanh Nguyen

  2. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montford University, The Gateway, Leicester, LE1 9BH, U.K.

    Shengxiang Yang

  3. Warwick Business School, University of Warwick, Coventry, CV4 7AL, U.K.

    Juergen Branke

  4. Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, University of Birmingham, Birmingham, B15 2TT, U.K.

    Xin Yao

Authors
  1. Trung Thanh Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengxiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Juergen Branke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Trung Thanh Nguyen .

Editor information

Editors and Affiliations

  1. , School of Computer Science and Informati, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  2. , School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Nguyen, T.T., Yang, S., Branke, J., Yao, X. (2013). Evolutionary Dynamic Optimization: Methodologies. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38416-5_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38415-8

  • Online ISBN: 978-3-642-38416-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation