Skip to main content

Evolutionary Design of Emergent Behavior

  • Chapter
Organic Computing

Part of the book series: Understanding Complex Systems ((UCS))

Summary

Most technical systems envisioned in organic computing are assumed to be complex, consisting of a large number of interacting components, self-organizing and exhibiting emergent behavior. As is argued in this chapter, a system’s emergent properties surface only after realization or during a simulation simulation of all interacting components. Thus, the usual “top-down top-down” and “bottom-up bottom-up” design paradigmsparadigm have severe limitations when it comes to emergenceInstead, the use of evolutionary computation is advocated for the automated, simulation-based design of organic computing systems with emergent behavior.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. E. Aarts and J. Korst. Simulated annealing and Boltzmann machines. Wiley, 1989.

    Google Scholar 

  2. A. N. Aizawa and B. W. Wah. Scheduling of genetic algorithms in a noisy environment. Evolutionary Computation, pages 97–122, 1994.

    Google Scholar 

  3. E. Alba and M. Tomassini. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 6(5):443–461, 2002.

    Article  Google Scholar 

  4. L. A. Albert and D. E. Goldberg. Efficient evaluation genetic algorithms under integrated fitness functions. Technical Report 2001024, Illinois Genetic Algorithms Laboratory, Urbana-Champaign, USA, 2001.

    Google Scholar 

  5. C. Anderson. Creation of desirable complexity: strategies for designing self-organized systems. In D. Braha et al., editors, Complex Engineered Systems, pages 101–121. Springer, 2006.

    Google Scholar 

  6. D. V. Arnold and H.-G. Beyer. Efficiency and mutation strength adaptation of the μ/μi,λ)-ES in a noisy environment. In Schoenauer et al. 62, pages 39–48.

    Google Scholar 

  7. D. V. Arnold and H.-G. Beyer. Local performance of the (μ/μi,λ)-ES in a noisy environment. In W. Martin and W. Spears, editors, Foundations of Genetic Algorithms, pages 127–142. Morgan Kaufmann, 2000.

    Google Scholar 

  8. D. V. Arnold and H.-G. Beyer. A comparison of evolution strategies with other direct search methods in the presence of noise. Computational Optimization and Applications, 24:135–159, 2003.

    Article  MathSciNet  MATH  Google Scholar 

  9. C. Bernon, V. Camps, M.-P. Gleizes, and G. Picard. Tools for self-organizing applications engineering. In G. D. Serugendo et al., editors, Engineering Self-Organizing Systems, volume 2977 of LNAI, page 283.298. Springer, 2004.

    Google Scholar 

  10. H.-G. Beyer. Toward a theory of evolution strategies: Some asymptotical results from the (1+,λ)-theory. Evolutionary Computation, 1(2):165–188, 1993.

    Article  Google Scholar 

  11. J. Boesel. Search and Selection for Large-Scale Stochastic Optimization. PhD thesis, Northwestern University, Evanston, Illinois, USA, 1999.

    Google Scholar 

  12. E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm intelligence: From natural to artificial systems. Oxford University Press, 1999.

    Google Scholar 

  13. J. Branke. Creating robust solutions by means of an evolutionary algorithm. In A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature, volume 1498 of LNCS, pages 119–128. Springer, 1998.

    Google Scholar 

  14. J. Branke.Evolutionary Optimization in Dynamic Environments. Kluwer, 2001.

    Google Scholar 

  15. J. Branke. Reducing the sampling variance when searching for robust solutions. In L. S. et al., editor, Genetic and Evolutionary Computation Conference (GECCO’01), pages 235–242. Morgan Kaufmann, 2001.

    Google Scholar 

  16. J. Branke, S. Chick, and C. Schmidt. Selecting a selection procedure. Technical report, Fontainebleau, 2005.

    Google Scholar 

  17. J. Branke and K. Deb. Integrating user preferences into evolutionary multi-objective optimization. In Y. Jin, editor, Knowledge Incorporation into Evolutionary Algorithms, pages 461–478. Springer, 2004.

    Google Scholar 

  18. J. Branke, K. Deb, H. Dierolf, and M. Osswald. Finding knees in multi-objective optimization. In Parallel Problem Solving from Nature, number 3242 in LNCS, pages 722–731. Springer, 2004.

    Google Scholar 

  19. J. Branke, P. Funes, and F. Thiele. Evolving en-route caching strategies for the internet. In Genetic and Evolutionary Computation Conference, volume 3103 of LNCS, pages 434–446. Springer, 2004.

    Google Scholar 

  20. J. Branke, A. Kamper, and H. Schmeck. Distribution of evolutionary algorithms in heterogeneous networks. In Genetic and Evolutionary Computation Conference, volume 3102 of LNCS, pages 923–934, 2004.

    Google Scholar 

  21. J. Branke, T. Kaußler, and H. Schmeck. Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32(6):499–508, 2001.

    Article  MATH  Google Scholar 

  22. J. Branke, M. Mnif, C. Müller-Schloer, H. Prothmann, U. Richter, F. Rochner, and H. Schmeck. Organic computing - addressing complexity by controlled self-organization. In International Symposium on Leveraging Applications of Formal Methods, Verification and Validation. ACM, 2007.

    Google Scholar 

  23. J. Branke and C. Schmidt. Selection in the presence of noise. In E. Cantu-Paz, editor, Genetic and Evolutionary Computation Conference, volume 2723 of LNCS, pages 766–777. Springer, 2003.

    Google Scholar 

  24. J. Branke and C. Schmidt. Sequential sampling in noisy environments. In X. {Yao et al., editor, Parallel Problem Solving from Nature, volume 3242 of LNCS, pages 202–211. Springer, 2004.

    Google Scholar 

  25. J. Branke, C. Schmidt, and H. Schmeck. Efficient fitness estimation in noisy environments. In L. Spector et al., editors, Genetic and Evolutionary Computation Conference, pages 243–250. Morgan Kaufmann, 2001.

    Google Scholar 

  26. E. Cantu-Paz. Efficient and Accurate Parallel Genetic Algorithms. Kluwer, 2000.

    Google Scholar 

  27. I. Das. On characterizing the ’knee’ of the pareto curve based on normal-boundary intersection. Structural Optimization, 18(2/3):107–115, 1999.

    Google Scholar 

  28. T. {De Wolf and T. Holvoet. Emergence versus self-organization: Different concepts but promising when combined. In S. A. Brueckner et al., editors, Engineering Self-Organising Systems: Methodologies and Applications, number 3464 in LNCS, pages 1–15. Springer, 2005.

    Google Scholar 

  29. T. De Wolf and T. Holvoet. Towards a methodology for engineering self-organising emergent systems. In H. Czap et al., editors, Self-Organization and Autonomic Informatics, pages 18–34. Springer, 2005.

    Google Scholar 

  30. K. Deb. Solving goal programming problems using multi-objective genetic algorithms. In Congress on Evolutionary Computation, volume 1, pages 77–84. IEEE, 1999.

    Google Scholar 

  31. K. Deb. Multi-Objective Optimization using Evolutionary Algorithms. Wiley, 2001.

    Google Scholar 

  32. K. A. DeJong. Evolutionary Computation. MIT Press, 2002.

    Google Scholar 

  33. R. C. Eberhart and Y. Shi. Swarm Intelligence. Morgan Kaufmann, 2001.

    Google Scholar 

  34. B. Edmonds. Using the experimental method to produce reliable self-organised systems. In S. Brueckner et al., editors, Engineering Self-Organising Systems, volume 3464 of LNAI, pages 84–99. Springer, 2005.

    Google Scholar 

  35. A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Springer, 2003.

    Google Scholar 

  36. Repository on multi-objective evolutionary algorithms. online, url{http://www.lania.mx/simccoello/EMOO/.

  37. J. M. Fitzpatrick and J. J. Grefenstette. Genetic algorithms in noisy environments. Machine Learning, 3:101–120, 1988.

    Google Scholar 

  38. L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley, 1966.

    Google Scholar 

  39. M. C. Fu. Optimizationn for simulation: Theory vs. practice. INFORMS Journal of Computing, 14(3):192–215, 2002.

    Article  MATH  Google Scholar 

  40. F. Glover. Tabu search - part I. ORSA Journal of Computing, 1(3):190–206, 1989.

    Article  MATH  Google Scholar 

  41. P. Goldate. Optimierung einer Ampelsteuerung mit Hilfe von evolutionären Algorithmen. Master’s thesis, Institute AIFB, University of Karlsruhe, 76128 Karlsruhe, Germany, August 2003.

    Google Scholar 

  42. D. E. Goldberg. Genetic Algorithms. Addison-Wesley, 1989.

    Google Scholar 

  43. A. Gosavi. Simulation-based optimization. Kluwer Academic, 2003.

    Google Scholar 

  44. H. Greiner. Robust optical coating design with evolutionary strategies. Applied Optics, 35(28):5477–5483, 1996.

    Article  ADS  Google Scholar 

  45. U. Hammel and T. Bäck. Evolution strategies on noisy functions, how to improve convergence properties. In Y. Davidor, H. P. Schwefel, and R. Männer, editors, Parallel Problem Solving from Nature, volume 866 of LNCS. Springer, 1994.

    Google Scholar 

  46. J. Holland. Emergence - From chaos to order. Addison-Wesley, 1998.

    Google Scholar 

  47. Y. Jin. A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9:3–12, 2005.

    Article  Google Scholar 

  48. Y. Jin and J. Branke. Evolutionary optimization in uncertain environments – a survey. IEEE Transactions on Evolutionary Computation, 9(3):303–317, 2005.

    Article  Google Scholar 

  49. S.-H. Kim and B. Nelson. A fully sequential procedure for indifference-zone selection in simulation. ACM Transactions on Modelin and Computer Simulation, 11(3):251–273, 2001.

    Article  Google Scholar 

  50. J. R. Koza. Genetic Programming. MIT Press, 1991.

    Google Scholar 

  51. B. L. Miller. Noise, Sampling, and Efficient Genetic Algorithms. PhD thesis, Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1997. available as TR 97001.

    Google Scholar 

  52. B. L. Miller and D. E. Goldberg. Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation, 4(2):113–131, 1996.

    Article  Google Scholar 

  53. A. A. Minai, D. Braha, and Y. Bar-Yam. Complex engineered systems: A new paradigm. In D. Braha et al., editors, Complex Engineered Systems, pages 1–21. Springer, 2006.

    Google Scholar 

  54. R. Nagpal. A catalog of biologically-inspired primitives for engineering self-organization. In G. D. Serugendo et al., editors, Engineering Self-Organizing Systems, volume 2977 of LNAI, pages 53–62. Springer, 2004.

    Google Scholar 

  55. I. Paenke, J. Branke, and Y. Jin. Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation, 10(4):405–420, 2006.

    Article  Google Scholar 

  56. Parabon Inc. Company homepage. Online. http://www.parabon.com.

  57. H. V. D. Parunak. “go to the ant”: Engineering principles from natural multi-agent systems. Annals of Operations Research, 75:69–101, 1997.

    Article  MATH  Google Scholar 

  58. I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipen der biologischen Evolution. Frommann-Holzboog, Stuttgart, 1973.

    Google Scholar 

  59. Y. Sano and H. Kita. Optimization of noisy fitness functions by means of genetic algorithms using history of search. In Schoenauer et al. pages 571–580.

    Google Scholar 

  60. Y. Sano and H. Kita. Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation. In Congress on Evolutionary Computation, pages 360–365. IEEE Press, 2002.

    Google Scholar 

  61. H. Schmeck, U. Kohlmorgen, and J. Branke. Parallel implementations of evolutionary algorithms. In A. Zomaya, F. Ercal, and S. Olariu, editors, Solutions to Parallel and Distributed Computing Problems, pages 47–66. Wiley, 2000.

    Google Scholar 

  62. M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, editors. Parallel Problem Solving from Nature, volume 1917 of LNCS. Springer, 2000.

    Google Scholar 

  63. T. Schöler and C. Müller-Schloer. An observer/controller architecture for adaptive reconfigurable stacks. In M. Beigl and P. Lukowicz, editors, International Conference on Architecture Of Computing Systems, volume 3432 of LNCS, pages 139–153. Springer, 2005.

    Google Scholar 

  64. H.-P. Schwefel. Evolutionsstrategie und numerische Optimierung. PhD thesis, Technische Universität Berlin, Germany, 1975.

    Google Scholar 

  65. Seti@home. Project homepage.Online.http://setiathome.ssl.berkeley.edu/.

  66. J. C. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Transactions on Automatic Control, 31:332–341, 1992.

    Article  MathSciNet  Google Scholar 

  67. J. C. Spall. Introduction to stochastic search and optimization. John Wiley and Sons, 2003.

    Google Scholar 

  68. P. Stagge. Averaging efficiently in the presence of noise. In A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature V, volume 1498 of LNCS, pages 188–197. Springer, 1998.

    Google Scholar 

  69. X. Tang and S. T. Chanson. Coordinated en-route web caching. IEEE Transactions on Computers, 51(6):595–607, 2002.

    Article  Google Scholar 

  70. J. Teich. Pareto-front exploration with uncertain objectives. In E. Zitzler, K. Deb, L. Thiele, C. A. C. Coello, and D. Corne, editors, Evolutionary Multi-Criterion Optimization, volume 1993 of LNCS, pages 314–328. Springer, 2001.

    Google Scholar 

  71. A. Thompson. On the automatic design of robust elektronics through artificial evolution. In M. Sipper, D. Mange, and A. Peres-Urike, editors, International Conference on Evolvable Systems, pages 13 – 24. Springer, 1998.

    Google Scholar 

  72. D. S. Todd and P. Sen. Directed multiple objective search of design spaces using genetic algorithms and neural networks. In W. B. et al., editor, Genetic and Evolutionary Computation Conference, pages 1738–1743. Morgan Kaufmann, San Francisco, California, 1999.

    Google Scholar 

  73. S. Tsutsui and A. Ghosh. Genetic algorithms with a robust solution searching scheme. IEEE Transactions on Evolutionary Computation, 1(3):201–208, 1997.

    Article  Google Scholar 

  74. United Devices. Company homepage. Online. http://www.ud.com.

    Google Scholar 

  75. D. Wiesmann, U. Hammel, and T. Bäck. Robust design of multilayer optical coatings by means of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2(4):162–167, 1998.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Branke, J., Schmeck, H. (2009). Evolutionary Design of Emergent Behavior. In: Organic Computing. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77657-4_6

Download citation

Publish with us

Policies and ethics