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Dynamic Problems and Nature Inspired Meta-heuristics

  • Tim Hendtlass
  • Irene Moser
  • Marcus Randall
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 210)

Abstract

Biological systems have often been used as the inspiration for search techniques to solve continuous and discrete combinatorial optimisation problems. One of the key aspects of biological systems is their ability to adapt to changing environmental conditions. Yet, biologically inspired optimisation techniques are mostly used to solve static problems (problems that do not change while they are being solved) rather than their dynamic counterparts. This is mainly due to the fact that the incorporation of temporal search control is a challenging task. Recently, however, a greater body of work has been completed on enhanced versions of these biologically inspired meta-heuristics, particularly genetic algorithms, ant colony optimisation, particle swarm optimisation and extremal optimisation, so as to allow them to solve dynamic optimisation problems. This survey chapter examines representative works and methodologies of these techniques on this important class of problems.

Keywords

Genetic Algorithm Particle Swarm Optimisation Particle Swarm Dynamic Problem Mobile Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Angeline, P.: 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)CrossRefGoogle Scholar
  2. 2.
    Angus, D., Hendtlass, T.: Ant Colony Optimisation Applied to a Dynamically Changing Problem. In: Hendtlass, T., Ali, M. (eds.) IEA/AIE 2002. LNCS (LNAI), vol. 2358, pp. 618–627. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Aydin, M., Öztemel, E.: Dynamic job shop scheduling using reinforcement learning agents. Robotics and Autonomous Systems 33, 169–178 (2000)CrossRefGoogle Scholar
  4. 4.
    Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: An explanation of 1/f noise. Physical Review Letters 59, 381–384 (1987)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Beasley, J.: OR-Library: Distributing test problems by electronic mail. Journal of the Operational Research Society 41(11), 1069–1072 (1990)CrossRefGoogle Scholar
  6. 6.
    Beasley, J., Krishnamoorthy, M., Sharaiha, Y., Abramson, D.: Scheduling aircraft landings - the static case. Transportation Science 34, 180–197 (2000)zbMATHCrossRefGoogle Scholar
  7. 7.
    Beasley, J., Krishnamoorthy, M., Sharaiha, Y., Abramson, D.: The displacement problem and dynamically scheduling aircraft landings. Journal of the Operational Research Society 55, 54–64 (2004)zbMATHCrossRefGoogle Scholar
  8. 8.
    Bendtsen, C., Krink, T.: Dynamic memory model for non-stationary optimisation. In: Proceedings of the Congress on Evolutionary Computation, pp. 992–997 (2002)Google Scholar
  9. 9.
    Bird, S., Li, X.: Adaptively choosing niching parameters in a PSO. In: Proceedings of the 8th Conference on Genetic and Evolutionary Computation, pp. 3–10 (2006)Google Scholar
  10. 10.
    Blackwell, T., Bentley, P.: Dynamic search with charged swarms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 19–26 (2002)Google Scholar
  11. 11.
    Blackwell, T., Branke, J.: Multi-swarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10, 459–472 (2006)CrossRefGoogle Scholar
  12. 12.
    Boettcher, S., Percus, A.: Extremal optimization: Methods derived from co-evolution. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 825–832. Morgan Kaufmann, San Francisco (1999)Google Scholar
  13. 13.
    Boettcher, S., Percus, A.: Nature’s way of optimizing. Artificial Intelligence 119, 275–286 (2000)zbMATHCrossRefGoogle Scholar
  14. 14.
    Boettcher, S., Percus, A.: Optimization with extremal dynamics. Physical Review Letters 86, 5211–5214 (2001)CrossRefGoogle Scholar
  15. 15.
    Bosman, P., La Poutré, H.: Inventory management and the impact of anticipation in evolutionary stochastic online dynamic optimization. In: Proceedings of the Congress on Evolutionary Computation, pp. 268–275. IEEE Computer Society Press, Los Alamitos (2007)CrossRefGoogle Scholar
  16. 16.
    Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the Congress on Evolutionary Computation, pp. 6–9. IEEE Computer Society Press, Los Alamitos (1999)Google Scholar
  17. 17.
    Branke, J.: The moving peaks benchmark (1999), http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/
  18. 18.
    Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, Norwell (2001)Google Scholar
  19. 19.
    Brits, R., Englebrecht, A., van der Bergh, F.: A niching particle swarm optimiser. In: Proceedings of the Asia Pacific Conference on Simulated Evolution and Learning, pp. 692–696 (2002)Google Scholar
  20. 20.
    Carlisle, A., Dozier, G.: Adapting particle swarm optimization to dynamic environments. In: Proceedings of the International Conference on Artificial Intelligence, pp. 429–434 (2000)Google Scholar
  21. 21.
    Carlisle, A., Dozier, G.: Tracking changing extrema with adaptive particle swarm optimizer. In: Proceedings of the World Automation Congress, pp. 265–270 (2002)Google Scholar
  22. 22.
    Chaudhry, S., Luo, W.: Application of genetic algorithms in production and operations management: A review. International Journal of Production Research 43(19), 4083–4101 (2005)zbMATHCrossRefGoogle Scholar
  23. 23.
    Cicirello, V., Smith, S.: Ant colony control for autonomous decentralized shop floor routing. In: Proceedings of the 5th International Symposium on Autonomous Decentralized Systems, pp. 383–390. IEEE Computer Society Press, Los Alamitos (2001)CrossRefGoogle Scholar
  24. 24.
    Cobb, H.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical Report AIC-90-001, Naval Research Laboratory, Washington, USA (1990)Google Scholar
  25. 25.
    Cobb, H., Grefenstette, J.: Genetic algorithms for tracking changing environments. In: Proceedings of the 5th International Conference on Genetic Algorithms, pp. 523–530. Morgan Kaufmann, San Francisco (1993)Google Scholar
  26. 26.
    De Jong, K.: An analysis of the behaviour of a class of genetic adaptive systems. PhD dissertation, University of Michigan (1975)Google Scholar
  27. 27.
    Di Caro, G., Dorigo, M.: AntNet: A mobile agents approach to adaptive routing. Tech. Rep. IRIDIA/97-12, Université Libre de Bruxelles, Belgium (1997)Google Scholar
  28. 28.
    Di Caro, G., Dorigo, M.: An adaptive multi-agent routing algorithm inspired by ants behavior. In: Proceedings of 5th Annual Australasian Conference on Parallel Real Time Systems, pp. 261–272 (1998)Google Scholar
  29. 29.
    Di Caro, G., Dorigo, M.: Ant colonies for adaptive routing in packet-switched communications networks. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 673–682. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  30. 30.
    Di Caro, G., Dorigo, M.: AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research 9, 317–365 (1998)zbMATHGoogle Scholar
  31. 31.
    Di Caro, G., Dorigo, M.: Mobile agents for adaptive routing. In: Proceedings of the 31st Annual Hawaii International Conference on System Sciences, pp. 74–83. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  32. 32.
    Di Caro, G., Dorigo, M.: Two ant colony algorithms for best-effort routing in datagram networks. In: Proceedings of the 10th IASTED International Conference on Parallel and Distributed Computing and Systems, pp. 541–546. IASTED/ACTA Press (1998)Google Scholar
  33. 33.
    Di Caro, G., Ducatalle, F., Gambardella, L.: AntHocNet: An ant-based hybrid routing algorithm for mobile ad hoc networks. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 461–470. Springer, Heidelberg (2004)Google Scholar
  34. 34.
    Di Caro, G., Ducatalle, F., Gambardella, L.: AntHocNet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transaction on Telecommunications - Special Issue on Self-organisation in Mobile Networking 16, 443–455 (2005)Google Scholar
  35. 35.
    Dorigo, M., Di Caro, G.: The ant colony optimization meta-heuristic. In: New Ideas in Optimization, pp. 11–32. McGraw-Hill, London (1999)Google Scholar
  36. 36.
    Dreo, J., Siarry, P.: An ant colony algorithm aimed at dynamic continuous optimization. Applied Mathematics and Computation 181, 457–467 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  37. 37.
    Dror, M., Powell, W.: Stochastic and dynamic models in transportation. Operations Research 41, 11–14 (1993)CrossRefGoogle Scholar
  38. 38.
    Ducatelle, F., Di Caro, G., Gambardella, L.: Ant agents for hybrid multipath routing in mobile ad hoc networks. In: Proceedings of Wireless On-demand Network Systems and Services, pp. 44–53 (2005)Google Scholar
  39. 39.
    Eberhart, R., Kennedy, J.: A new optimizer using particles swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43 (1995)Google Scholar
  40. 40.
    Eyckelhof, C., Snoek, M.: Ant systems for a dynamic TSP: Ants caught in a traffic jam. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 88–99. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  41. 41.
    Gauthier, S.: Solving the dynamic aircraft landing problem using ant colony optimisation. Masters Thesis, School of Information Technology, Bond University (2006)Google Scholar
  42. 42.
    Godwin, T., Gopalan, R., Narendran, T.: Locomotive assignment and freight train scheduling using genetic algorithms. International Transactions in Operational Research 13(4), 299–332 (2006)zbMATHCrossRefGoogle Scholar
  43. 43.
    Goldberg, D., Smith, R.: Nonstationary function optimization using genetic algorithm with dominance and diploidy. In: Proceedings of the 2nd International Conference on Genetic Algorithms on Genetic algorithms and their application, pp. 59–68. Lawrence Erlbaum Associates, Inc., Mahwah (1987)Google Scholar
  44. 44.
    Grefenstette, J.: Evolvability in dynamic fitness landscapes: A genetic algorithm approach. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 2031–2038. IEEE Press, Los Alamitos (1999)Google Scholar
  45. 45.
    Guntsch, M., Middendorf, M.: Pheromone modification strategies for ant algorithms applied to dynamic TSP. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 213–222. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  46. 46.
    Guntsch, M., Middendorf, M.: Applying population based ACO to dynamic optimization problems. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 111–122. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  47. 47.
    Guntsch, M., Middendorf, M.: A population based approach for ACO. In: Cagnoni, S., Gottlieb, J., Hart, E., Middendorf, M., Raidl, G.R. (eds.) EvoIASP 2002, EvoWorkshops 2002, EvoSTIM 2002, EvoCOP 2002, and EvoPlan 2002. LNCS, vol. 2279, pp. 72–81. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  48. 48.
    Guntsch, M., Middendorf, M., Schmeck, H.: An ant colony optimization approach to dynamic TSP. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 860–867. Morgan Kaufmann, San Francisco (2001)Google Scholar
  49. 49.
    Gutjahr, W., Rauner, M.: An ACO algorithm for a dynamic regional nurse-scheduling problem in Austria. Computers and Operations Research 34, 642–666 (2007)zbMATHCrossRefGoogle Scholar
  50. 50.
    Hadad, B., Eick, C.: Supporting polyploidy in genetic algorithms using dominance vectors. In: Angeline, P.J., McDonnell, J.R., Reynolds, R.G., Eberhart, R. (eds.) EP 1997. LNCS, vol. 1213, pp. 223–234. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  51. 51.
    Hendtlass, T.: WoSP: A multi-optima particle swarm algorithm. In: Proceedings of the Congress of Evolutionary Computing, pp. 727–734. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  52. 52.
    Heusse, M., Snyers, D., Guérin, S., Knutz, P.: Adaptive agent-driven routing and load balancing in communication networks. Adaptive Complex Systems 2, 1–15 (1998)Google Scholar
  53. 53.
    Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  54. 54.
    Hu, X., Eberhart, R.: Adaptive particle swarm optimisation: Detection and response to dynamic systems. In: Proceedings of the Congress on Evolutionary Computing, pp. 1666–1670. IEEE Press, Los Alamitos (2002)Google Scholar
  55. 55.
    Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer. In: Proceedings of the Congress on Evolutionary Computing, pp. 1666–1670. IEEE Press, Los Alamitos (2003)Google Scholar
  56. 56.
    Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer for dynamic optimization problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)Google Scholar
  57. 57.
    Karaman, A., Uyar, S., Eryigit, G.: The memory indexing evolutionary algorithm for dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 563–573. Springer, Heidelberg (2005)Google Scholar
  58. 58.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE Conference on Neural Networks, pp. 1942–1947 (1995)Google Scholar
  59. 59.
    Kókai, G., Christ, T., Frühauf, H.: Using hardware-based particle swarm method for dynamic optimization of adaptive array antennas. In: Proceedings of Adaptive Hardware and Systems, pp. 51–58 (2006)Google Scholar
  60. 60.
    Liles, W., De Jong, K.: The usefulness of tag bits in changing environments. In: Proceedings of the Congress on Evolutinary Computation, vol 3, pp. 2054–2060 (1999)Google Scholar
  61. 61.
    Louis, S., Johnson, J.: Solving similar problems using genetic algorithms and case-based memory. In: Bäck, T. (ed.) Proceedings of the 7th International Conference on Genetic Algorithms, pp. 283–290 (1997)Google Scholar
  62. 62.
    McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 321–326 (1997)Google Scholar
  63. 63.
    Menai, M.: An Evolutionary Local Search Method for Incremental Satisfiability. In: Buchberger, B., Campbell, J. (eds.) AISC 2004. LNCS, vol. 3249, pp. 143–156. Springer, Heidelberg (2004)Google Scholar
  64. 64.
    Meng, Y., Kazeem, Q., Muller, J.: A hybrid ACO/PCO control algorithm for distributed swarm robots. In: Proceedings of the IEEE Swarm Intelligence Symposium, pp. 273–280 (2007)Google Scholar
  65. 65.
    Morrison, R., De Jong, K.: A test problem generator for non-stationary environments. In: Proceedings of the Congress on Evolutionary Computation, pp. 2047–2053 (1999)Google Scholar
  66. 66.
    Moser, I.: Applying extremal optimisation to dynamic optimisation problems. PhD in information technology, Swinburne University of Technology. Faculty of Information and Communication Technologies (2008)Google Scholar
  67. 67.
    Moser, I., Hendtlass, T.: Solving dynamic single-runway aircraft landing problems with extremal optimisation. In: Proceedings of the IEEE Symposium on Computational Intelligence in Scheduling, pp. 206–211 (2007)Google Scholar
  68. 68.
    Mullen, P., Monson, C., Seppi, K.: Particle swarm optimization in dynamic pricing. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1232–1239 (2006)Google Scholar
  69. 69.
    Ng, K., Wong, K.: A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 159–166. Morgan Kaufmann, San Francisco (1995)Google Scholar
  70. 70.
    Parsopoulos, K., Vrahatis, M.: UPSO: A unified particle swarm optimization scheme. In: Proceedings of the International Conference on Computational Methods in Sciences and Engineering, pp. 868–873 (2004)Google Scholar
  71. 71.
    Parsopoulos, K., Vrahatis, M.: Unified particle swarm optimization in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Corne, D.W., Drechsler, R., Jin, Y., Machado, P., Marchiori, E., Romero, J., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2005. LNCS, vol. 3449, pp. 590–599. Springer, Heidelberg (2005)Google Scholar
  72. 72.
    Pekala, M., Schuster, E.: Dynamic optimization of a heterogeneous swarm of robots. In: Proceedings of the 10th IASTED International Conference on Intelligent Systems and Control, pp. 354–359 (2007)Google Scholar
  73. 73.
    Perkins, C., Royer, E.: Ad-hoc on-demand distance vector routing. In: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications, pp. 90–100 (1999)Google Scholar
  74. 74.
    Ramsey, C., Grefenstette, J.: Case-based initialization of genetic algorithms. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, pp. 84–91 (1993)Google Scholar
  75. 75.
    Randall, M.: A dynamic optimisation approach for ant colony optimisation using the multidimensional knapsack problem. In: Recent Advances in Artificial Life, Advances in Natural Computation, vol. 3, pp. 215–226. World Scientific, Singapore (2005)CrossRefGoogle Scholar
  76. 76.
    Saleh, M., Ghani, A.: Adaptive routing in packet-switched networks using agents updating methods. Malaysian Journal of Computer Science 16, 1–10 (2003)Google Scholar
  77. 77.
    Schoonderwoerd, R., Holland, O., Bruten, J., Rothkrantz, L.: Ant-based load balancing in telecommunications networks. Adaptive Behavior 2, 169–207 (1996)Google Scholar
  78. 78.
    Simões, A., Costa, E.: An immune-system-based genetic algorithm to deal with dyamic environments: Diversity and memory. In: Proceedings of the 6th International Conference on Artificial Neural Networks, pp. 168–174 (2003)Google Scholar
  79. 79.
    Simões, A., Costa, E.: Improving memory-based evolutionary algorithms in changing environments. Technical Report TR2007/004, CISUC (2007)Google Scholar
  80. 80.
    Simões, A., Costa, E.: Improving memory’s usage in evolutionary algorithms for changing environments. In: Proceedings of the Congress on Evolutionary Computation, pp. 276–283. IEEE Press, Los Alamitos (2007)CrossRefGoogle Scholar
  81. 81.
    Simões, A., Costa, E.: Variable-size memory evolutionary algorithm to deal with dynamic environments. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 617–626. Springer, Heidelberg (2007)Google Scholar
  82. 82.
    Simões, A., Costa, E.: VMEA: Studies of the impact of different replacing strategies in the algorithm’s performance and in the population’s diversity when dealing with dynamic environments. Technical Report TR2007/001, CISUC (2007)Google Scholar
  83. 83.
    Subramanian, D., Druschel, P., Chen, J.: Ants and reinforcement learning: A case study in routing in dynamic networks. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, pp. 832–838 (1997)Google Scholar
  84. 84.
    Svenson, P.: Extremal optimization for sensor report pre-processing. In: Proceedings of Signal Processing, Sensor Fusion, and Target Recognition XIII, pp. 162–171 (2004)Google Scholar
  85. 85.
    Tinós, R., Yang, S.: Genetic algorithms with self-organized criticality for dynamic optimisation problems. The IEEE Congress on Evolutionary Computation 3, 2816–2823 (2005)CrossRefGoogle Scholar
  86. 86.
    Tinós, R., Yang, S.: A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genetic Programming and Evolvable Machines 8(3), 255–286 (2007)CrossRefGoogle Scholar
  87. 87.
    Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proceedings of the Congress on Evolutionary Computation, vol. 3, pp. 1843–1850. IEEE Press, Los Alamitos (1999)Google Scholar
  88. 88.
    Ursem, R.: Multinational GAs: Multimodal optimization techniques in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 19–26 (2000)Google Scholar
  89. 89.
    Varela, G., Sinclair, M.: Ant colony optimisation for virtual-wavelength-path routing and wavelength allocation. In: Proceedings of the Congress on Evolutionary Computation (1999)Google Scholar
  90. 90.
    White, T., Pagurek, B., Oppacher, F.: Connection management by ants: An application of mobile agents in network management. In: Proceedings of Combinatorial Optimization (1998)Google Scholar
  91. 91.
    Xia, Y., Chen, J., Meng, X.: On the dynamic ant colony algorithm optimization based on multi-pheromones. In: Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, pp. 630–635 (2008)Google Scholar
  92. 92.
    Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1115–1122. ACM, New York (2005)CrossRefGoogle Scholar
  93. 93.
    Yang, S.: Associative memory scheme for genetic algorithms in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 788–799. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  94. 94.
    Yang, S.: A comparative study of immune system based genetic algorithms in dynamic environments. In: Proceedings of the 8th Conference on Genetic and Evolutionary Computation, pp. 1377–1384. ACM, New York (2006)CrossRefGoogle Scholar
  95. 95.
    Yang, S.: Genetic algorithms with elitism-based immigrants for changing optimization problems. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 627–636. Springer, Heidelberg (2007)Google Scholar
  96. 96.
    Yang, S., Tinós, R.: A hybrid immigrants scheme for genetic algorithms in dynamic environments. International Journal of Automation and Computing 4, 243–254 (2007)CrossRefGoogle Scholar
  97. 97.
    Zhang, X., Li, X., Li, Y., Zhou, Y., Zhang, J., Zhang, N., Wu, B., Yuan, T., Chen, L., Zhang, H., Yao, M., Yang, B.: Two-stage adaptive PMD compensation in 40 Gb/s OTDM optical communication system using PSO algorithm. Optical and Quantum Electronics 36, 1089–1104 (2004)CrossRefGoogle Scholar
  98. 98.
    Zhang, Y., Kuhn, L., Fromherz, M.: Improvements on ant routing for sensor networks. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 154–165. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Tim Hendtlass
    • 1
  • Irene Moser
    • 1
  • Marcus Randall
    • 2
  1. 1.Centre for Information Technology Research School of Information and Communication TechnologiesSwinburne University
  2. 2.School of Information TechnologyBond UniversityAustralia

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