Skip to main content
SpringerLink
Log in

Other Evolutionary Concepts

  • Chapter
Towards Hybrid and Adaptive Computing

Part of the book series: Studies in Computational Intelligence ((SCI,volume 307))

  • 867 Accesses

Abstract

Evolutionary Algorithms in numerous forms present powerful problem solving tools. These tools have been modified and adapted to various kinds of problems as per the requirements. Genetic Algorithms, Evolutionary Strategies and Genetic Programming happen to be the basic classifications of these algorithms. In this chapter we further discuss some of the widely used models of Evolutionary Algorithms that are extensively used for evolving systems and their optimization. All these differ in their methodology of problem solving. We first present Differential Evolution that uses the differences between individuals for their optimization. The chapter would then present Artificial Immune Systems that are an analogy from the natural immunity system prevalent to fight diseases. Here we discuss the self and non-self methodology of classification. Then we present Co-evolution where the different individuals of the population pool help each other for the evolution. The other topics of discussion include Cultural Algorithm where the evolution is biased by a culture or belief space. We also discuss about Cellular Automata in this chapter.

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 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. Abbass, H.: The Self-Adaptive Pareto Differential Evolution. In: Proc. Con. on Evol. Comput., pp. 831–836 (2002)

    Google Scholar 

  2. Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto-frontier Differential Evolution Approach for Multi-objective Optimization Problems. In: Proc. of the 2001 Cong. on Evol. Comput. (2001)

    Google Scholar 

  3. Aickelin, U., Cayzer, S.: The Danger Theory and Its Application to Artificial Immune Systems, pp. 141–148 (2002)

    Google Scholar 

  4. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems. IEEE Trans Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  5. Coello, C.A., Becerra, R.L.: Evolutionary Multiobjective Optimization using a Cultural Algorithm. In: IEEE Swarm Intell. Sympos., Piscataway, NJ, pp. 6–13 (2003)

    Google Scholar 

  6. Coello, C.A., Becerra, R.L.: Efficient Evolutionary Optimization through the use of a Cultural Algorithm. Engg. Optim. 36(2), 219–236 (2004)

    Article  Google Scholar 

  7. de Castro, L.N., Timmis, J.: An artificial immune network for multimodal function optimization. In: Proc. of the 2002 Cong. on Evol. Comput., vol. 1, pp. 699–704 (2002)

    Google Scholar 

  8. de Jong, K.A.: Evolutionary computation: a unified approach. MIT Press, Cambridge (2006)

    MATH  Google Scholar 

  9. Drossel, B., Schwabl, F.: Formation of space-time structure in a forest-fire model. Physica A: Statistical and Theoretical Physics 204(1-4), 212–229 (1994)

    Article  Google Scholar 

  10. Ficici, S.G., Pollack, J.B.: A Game-Theoretic Approach to the Simple Coevolutionary Algorithm. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 467–476. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  11. Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies (2008)

    Google Scholar 

  12. Forrest, S., Javornik, B., Smith, R.E., Perelson, A.S.: Using genetic algorithms to explore pattern recognition in the immune system. Evol. Comput. archive 1(3), 191–211 (1993)

    Article  Google Scholar 

  13. Fuks, H.: Solution of the density classification problem with two cellular automata rules. Physical Review E 55(3), R2081–R2084 (1997)

    Article  Google Scholar 

  14. Gardner, M.: On cellular automata, self-reproduction, the Garden of Eden and the game life. Scientific American 224(2), 112–117 (1971)

    Article  Google Scholar 

  15. Gasper, A., Collard, P.: From GAs to artificial immune systems: improving adaptation in time dependent optimization. In: Proc. of the 1999 Cong. on Evol. Comput., vol. 3, pp. 1859–1866 (1999a)

    Google Scholar 

  16. Ho, N.B., Tay, J.C.: GENACE: An Efficient Cultural Algorithm for Solving the Flexible Job-Shop Problem. In: Proc. Cong. on Evol. Comput., pp. 1759–1766 (2004)

    Google Scholar 

  17. Hofmeyr, S.A., Forrest, S.: Architecture for an Artificial Immune System. Evol. Comput. archive 8(4), 443–473 (2000)

    Article  Google Scholar 

  18. Jakob, V., Rene, T.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Proc. of the 2004 Cong. on Evol. Comput. (2004)

    Google Scholar 

  19. Kim, J., Bentley, P.J.: Towards an Artificial Immune System for Network Intrusion Detection: An Investigation of Dynamic Clonal Selection. In: Proc. of the 1999 Cong. on Evol. Comput., vol. 2, pp. 1244–1252 (2002)

    Google Scholar 

  20. Kim, J., Bentley, P.: Negative Selection and Niching by an Artificial Immune System for Network Intrusion Detection. In: Late Breaking Papers at the 1999 Genetic and Evol. Comput. Conf., pp. 149–158 (1999b)

    Google Scholar 

  21. Kim, J., Bentley, P.J.: An Evaluation of Negative Selection in an Artificial Immune System for Network Intrusion Detection. In: Proc. of the Genetic and Evol. Comput. Conf., GECCO, vol. 1, pp. 1330–1337 (2001)

    Google Scholar 

  22. Liu, J., Lampinen, J.: A Fuzzy Adaptive Differential Evolution Algorithm. Soft Comput. 9, 448–462 (2003)

    Article  Google Scholar 

  23. Maerivoet, S., de Moor, B.: Cellular automata models of road traffic. Physics Reports 419(1), 1–64 (2005)

    Article  MathSciNet  Google Scholar 

  24. Pedrajas, N.G., Martinez, C.H., Perez, J.M.: Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks). Neural Netw. archive 15(10), 1259–1278 (2002)

    Article  Google Scholar 

  25. Potter, M.A.: The design and analysis of a computational model of cooperative coevolution. PhD thesis, George Mason University (1997)

    Google Scholar 

  26. Qin, A.K., Suganthan, P.N.: Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE Cong. on Evol. Comput., vol. 2, pp. 1785–1791 (2005)

    Google Scholar 

  27. Reynolds, R.G.: An Introduction to Cultural Algorithms. In: Proc. of the 3rd Annual Conf. on Evol. Programming, pp. 131–139. World Scientific Publishing, Singapore (1994)

    Google Scholar 

  28. Reynolds, R.G., Chung, C.J.: A Self-adaptive Approach to Representation Shifts in Cultural Algorithms. In: Proc. of IEEE Intl. Conf. on Evol. Comput., Nagoya, Japan, pp. 94–99 (1996)

    Google Scholar 

  29. Rosin, C., Belew, R.: New Methods for Competitive Coevolution. Evol. Comput. 5, 1–29 (1996)

    Article  Google Scholar 

  30. Shi, Y., Krohling, R.A.: Co-evolutionary particle swarm optimization to solve min-max problems. In: Proc. of the Evol. Comput. Conf., pp. 1682–1687 (2002)

    Google Scholar 

  31. Stanley, K.O., Miikkulainen, R.: Competitive Coevolution through Evolutionary Complexification. J. Artif. Intell. Res. 21, 63–100 (2004)

    Google Scholar 

  32. Storn, R., Price, K.: Minimizing the real functions of the ICEC 1996 contest by Differential Evolution. In: IEEE Conf. on Evol. Comput., pp. 842–844 (1996)

    Google Scholar 

  33. Storn, R., Price, K.: Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  34. Toffoli, T.: Cellular automata as an alternative to (rather than an approximation of) differential equations in modeling physics. Physica D: Nonlinear Phenomena 10(1-2), 117–127 (1984)

    Article  MathSciNet  Google Scholar 

  35. Wiegand, R.P., de Jong, K.A., Liles, W.C.: Analyzing cooperative coevolution with evolutionary game theory. In: Proc. of the Evol. Comput. Conf., vol. 2, pp. 1600–1605 (2002)

    Google Scholar 

  36. Wolfram, S.: Statistical mechanics of cellular automata. Reviews of Modern Physics 55(3), 601–644 (1983)

    Article  MATH  MathSciNet  Google Scholar 

  37. Worsch, T.: Simulation of cellular automata. Future Generation Computer Systems 16(2-3), 157–170 (1999)

    Article  Google Scholar 

Download references

Authors
  1. Anupam Shukla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ritu Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rahul Kala
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Shukla, A., Tiwari, R., Kala, R. (2010). Other Evolutionary Concepts. In: Towards Hybrid and Adaptive Computing. Studies in Computational Intelligence, vol 307. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14344-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14344-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14343-4

  • Online ISBN: 978-3-642-14344-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation