Skip to main content
SpringerLink
Log in
Book cover

Knowledge Incorporation in Evolutionary Computation pp 195–214Cite as

Performance-Based Computation of Chromosome Lifetimes in Genetic Algorithms

  • Chapter

  • 1131 Accesses

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 167))

Summary

This chapter introduces a novel external-memory genetic algorithms strategy based on the use of chromosomes with multiple-generation lifetimes where the lifetime of a chromosome is determined based on its survival and the reproduction capacities. Chromosomes of near-average quality are assigned a lifetime and inserted into a library called the chromosome library. Chromosomes in this library are combined with the current population in the creation of next generation offspring individuals. At the end of each generation, the lifetimes of the individuals in the library and the candidates within the current generation are recalculated as a function of their fitness values, number of recombination operations involved, and the fitness values of their offspring in the next generation. The main motivation behind the performance based chromosome lifetiming strategy is to trace some of the untested search directions in the recombination of potentially promising solutions. Chromosome library is partially updated at the end of each generation and its size is limited by a maximum value. The proposed genetic algorithm strategy is applied to the solution of both stationary and nonstationary hard numerical and combinatorial optimization problems. It outperforms the conventional genetic algorithms and some of its well-known variants in all trials.

This is a preview of subscription content, 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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Holland, J.H.: Adaptation in Natural and Artificial Systems: An introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press, (1992).

    Google Scholar 

  2. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, (1989) .

    MATH  Google Scholar 

  3. Eshelman, L., Schaffer, J.: Foundations of Genetic Algorithms 2. In: L. Whitley (editor): pp. 187–202, Morgan Kaufmann Publishers, San Mateo, CA, (1993).

    Google Scholar 

  4. Back, T.: Evolutionary Algorithms in Theory and Practice, Oxford University Press, (1996).

    Google Scholar 

  5. Gen, M., Runwei, C.: Genetic Algorithms in Engineering Design, John Wiley & Sons. Inc., (1997) .

    Google Scholar 

  6. Miettinen, K., Neitaanmaki, P., Makela, M.M., Periaux, J.: Evolutionary Algorithms in Engineering and Computer Science, John Wiley & Sons Ltd., (1999).

    MATH  Google Scholar 

  7. Cantu-Paz, E., Mejia-Olvera, M.: Designing efficient master-slave parallel genetic algorithms, IlliGAL Report No. 97004, Illinois Genetic Algorithm Laboratory, Urbana, IL, (1997) .

    Google Scholar 

  8. Whitley, D., Starkweather, T.: Genitorll: A distributed genetic algorithm, Journal of Experimental and Theoretical Artificial Intelligence, (1990) .

    Google Scholar 

  9. Eggermont, J., Lenaerts, T.: Non-stationary function optimization using evolutionary algorithms with a case-based memory, Technical Report TR-2001–11, Leiden Institute of Advanced Computer Science, The Netherlands, 2001.

    Google Scholar 

  10. Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in GAs having continuou, time-dependent nonstationary environment. NRL Memorandum Report 6760, 1990.

    Google Scholar 

  11. Grefenstette, J.J.: Genetic algorithms for changing environments, in R. Maenner, B. Manderick (eds.): PPSN II, PP. 137–144, North Holland, 1992.

    Google Scholar 

  12. Ramsey, C.L., Grefenstette, J. J.: Case-based initialization of GAs, in Forest, S., (Editor): Proceedings of the Fifth International Conference on Genetic Algorithms, p. 84–91, San Mateo, CA, (1993).

    Google Scholar 

  13. Louis, S., Li, G.: Augmenting genetic algorithms with memory to solve traveling salesman problem, http://citeseer.nj.nec.com/louis97augmenting.html, 1997.

    Google Scholar 

  14. Louis, S. J., Johnson, J.: Solving similar problems using genetic algorithms and case-based memory, in Back, T., (Editor): Proceedings of the Seventh International Conference on Genetic Algorithms, p. 84–91, San Fransisco, CA, (1997).

    Google Scholar 

  15. Coevolutionary search among adversaries, PhD Thesis, University of California, San Diego, 1997.

    Google Scholar 

  16. Eggermont, J., Lenaerts, T.: Dynamic optimization using evolutionary algorithms with a case-based memory, http://citeseer.nj.nec.com/561263.html.

  17. Hartono P., Hashimoto, S.: Migrational GA that preserves solutions in nonstatic optimization problems, Proceedings of the 2001 IEEE Systems, Man, and Cybernetics Conference, pp. 255–260, 2001.

    Google Scholar 

  18. Simoes, A., Costa, E.: Using genetic algorithms to deal with dynamical environments: comparative study of several approaches based on promoting diversity, in W. B. Langton et al. (eds.): Proceedings of the genetic and evolutionary computation conference GECCO’02, Morgan Kaufmann, New York, 2002.

    Google Scholar 

  19. Simoes, A., Costa, E.: Using biological inspiration to deal with dynamic environments, Proceedings of the seventh international conference on soft computing MENDEL’2001, Czech Republic, 2001.

    Google Scholar 

  20. Simoes, A., Costa, E.: An immune system based genetic algorithm to deal with dynamic environments: diversity and memory, D.W. Pearson, N.C. Steele, R. Albrecht (eds.): Proceedings of the Sixth International Conference on Neural Networks and Genetic Algorithms ICANNGA’03, Springer-Verlag, pp.168–174, France, 2003.

    Google Scholar 

  21. Kita, H., Sano, Y.: Genetic algorithms for optimization of noisy fitness functions and adaptation to changing environments, http://www.statp.is.tohoku.ac.jp/kazu/SMAPIP/2003/hayashibara/Proceedings/HajimeKita.pdf.

  22. Hemert, J., Hoyweghen, C.: A futurist approach to dynamic environments, http://www.citeseer.nj.nec.com/486549.html,2001/486549.html, 2001.

  23. Bendtsen, C.N., Krink, T.: Dynamic memory model for nonstationary optimization, http://citeseer.nj.nec.com/bendtsen02dynamic.html, 2002.

    Google Scholar 

  24. Acan, A., Tekol, Y.: Chromosome reuse in genetic algorithms, in A Cantu-Paz at al. (eds.): Genetic and Evolutionary Computation Conference GECCO 2003, Springer-Verlag, pp.695–705, Chicago, 2003.

    Chapter  Google Scholar 

  25. Goldberg, D. E., Smith, R. E.: Non-stationary function optimization using genetic algorithms and with dominance and diploidy, Genetic Algorithms and their Applications: Proceedings of the Second International Conference on Genetic Algorithms, p. 217–223, (1987).

    Google Scholar 

  26. Lewis, J., Hart, E., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on non-stationary problems, in Eiben, A. E., Back, T., Schoenauer, M., Schwefel, H. (Editors): Parallel Problem Solving from Nature- PPSN V, p. 139–148, Berlin, (1998).

    Chapter  Google Scholar 

  27. Calabretta, N., Galbiati, R., Nolfi, S., Parisi, D.: Investigating the role of diploidy in simulated populations of evolving individuals, Electronic Proceedings of the 1997 European Conference on Artificial Life.

    Google Scholar 

  28. Kim, Y., Kim, Lee, Cho, Lee-Kwang : Winner take all strategy for a diploid genetic algorithm, The First Asian Conference on Simulated Evolution and Learning, 1996.

    Google Scholar 

  29. Collingwood, E., Corne, D., Ross, P.: Useful diversity via multiploidy, IEEE International Conference on Evolutionary Computing,Nagoya, Japan, 1996.

    Google Scholar 

  30. Osmera, P., Kvasnicka, V., Pospichal, J.: Genetic algorithms with diploid chromosomes, MENDEL’97, pp. 111–116, 1997.

    Google Scholar 

  31. Ryan, C., Collins, J. J.: Polygenic inheritance- a haploid scheme that can outperform diploidy, in Eiben, A. E., Back, T., Schoenauer, M., Schwefel, H. (Editors): Parallel Problem Solving from Nature- PPSNV, p. 178–187, Berlin, (1998)

    Chapter  Google Scholar 

  32. Ryan, C.: The degree of oneness, First Online Workshop on Soft Computing, Aug. 19–30, (1996).

    Google Scholar 

  33. Goldberg, D. E., Deb, K., Korb, B.: Messy Genetic Algorithms: Motivation, analysis, and the first results, Complex Systems, Vol. 3, No. 5, p. 493–530, (1989).

    MathSciNet  MATH  Google Scholar 

  34. Goldberg, D. E., Deb, K., Kargupta, H., Harik, G.: Rapid, accurate optimization of difficult problems using fast messy genetic algorithms, in Forrest, S. (editor): Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 56–64, Morgan Kaufmann, 1993.

    Google Scholar 

  35. Dasgupta, D., McGregor, D.R.: sGA: a structured genetic algorithm, http://citeseer.nj.nec.com/dasgupta92sga.html, 1992.

    Google Scholar 

  36. Dasgupta, D., McGregor, D.R.: Nonstationary function optimization using the structured genetic algorithm, Proceedings of Paralle Problem Solving from Nature PPSN II, pp. 145–154, Brussels, 1992.

    Google Scholar 

  37. Luger, G.F.: Artificial Intelligence, 4th edition, Addison-Wesley, (2002).

    Google Scholar 

  38. S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, PrenticeHall, (1995).

    Google Scholar 

  39. http://www.f.utb.cz/people/zelinka/soma/func.html.

  40. Funabiki N., Takefuji Y.: A Neural network parallel algorithm for channel assignment problem in cellular radio networks, IEEE Trans. On Vehicular Technology, Vol. 41, No.4, p. 430–437, 1992.

    Article  Google Scholar 

  41. Beckmann D., Killat U.: A new strategy for the application genetic algorithms to the channel assignment problem, IEEE Trans. On Vehicular Technology, Vol. 48, No. 4, p. 1261–1269, 1999.

    Article  Google Scholar 

  42. Ngo C.Y., Li V.O.K.: Fixed channel assignment in cellular networks using a modified genetic algorithm, IEEE Trans. On Vehicular Technology, Vol. 47, No. 1, p. 163–172, 1998.

    Article  Google Scholar 

  43. Chakraborty G.: An efficient heuristic algorithm for channel assignment problem in cellular radio networks” , IEEE Trans. On Vehicular Technology, Vol. 50, No. 6, p. 1528–1539, 2001.

    Article  Google Scholar 

  44. Battiti R.: A randomized saturation degree heuristic for channel assignment in cellular radio networks, IEEE Trans. On Vehicular Technology, Vol. 50, No.2, p. 364–374, 2001.

    Article  Google Scholar 

  45. Wang W., Rushforth C.K.: An adaptive local search algorithm for the channel assignment problem” , IEEE Trans. On Vehicular Technology, Vol. 45, No.3, p. 459–466, 1996.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Dept., Eastern Mediterranean University Gazimagusa, T.R.N.C. Mersin 10, Turkey

    Adnan Acan & Yüce Tekol

Authors
  1. Adnan Acan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yüce Tekol
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Honda Research Institute Europe GmbH, Carl-Legien-Str.30, D-63073, Offenbach/Main, Germany

    Yaochu Jin

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Acan, A., Tekol, Y. (2005). Performance-Based Computation of Chromosome Lifetimes in Genetic Algorithms. In: Jin, Y. (eds) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol 167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44511-1_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-44511-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-06174-5

  • Online ISBN: 978-3-540-44511-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation