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A Selectionless Two-Society Multiple-Deme Approach for Parallel Genetic Algorithms

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2869))

Abstract

A novel multi-deme parallel genetic algorithm approach that eliminates the use of the selection operator by using multiple populations separated into two societies is introduced. Each individual population contains two subpopulations, one in each society, and individuals in one society are superior in fitness to the ones in the other and the size of subpopulations in each society is dynamically determined based on the average fitness value. The fitness-based division of individuals into two social subpopulations is based on the fact that, due to fitness-based selection procedures, most of the recombination operations take place among individuals with an above-average fitness value. Unidirectional synchronous migration of individuals is carried between populations in the same society and in the two societies. The proposed algorithm is applied for the solution of hard numerical and combinatorial optimization problems, and it outperforms the standard genetic algorithm implementation in all trials.

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References

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

    MATH  Google Scholar 

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

    Google Scholar 

  3. Nowostawski, M., Poli, R.: Review and Taxonomy of Parallel Genetic Algorithms, Technical Report: CSRP-99-11, The University of Otago, New Zeeland (May 1999)

    Google Scholar 

  4. Cantu-Paz, E.: A Survey of Parallel Genetic Algorithms, IlliGAL Report, University of Illinois at Urbana-Champaign, USA (1999)

    Google Scholar 

  5. Cantu-Paz, E.: Designing Efficient Master-Slave Parallel Genetic Algorithms, Illi- GAL Report:97004, University of Illinois at Urbana-Champaign, USA (1997)

    Google Scholar 

  6. Manderick, B., Spieesens, P.: Fine-Grained Parallel Genetic Algorithms. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 428–433. Morgan Kaufmann, San Francisco (1989)

    Google Scholar 

  7. Lin, S.-C., Punch, W., Goodman, E.: Coarse-Grain Parallel Genetic Algorithms: Categorization and New Approaches. In: IEEE Symposium on Parallel and Distributed Processing, IEEE CS Press, Los Alamitos (1994)

    Google Scholar 

  8. Cantu-Paz, E.: Topologies, Migration Rates, and Multi-Population Parallel Genetic Algorithms, IlliGAL Report:99007, University of Illinois at Urbana-Champaign, USA (1999)

    Google Scholar 

  9. Hauser, R., Manner, R.: Implementation of Standard Genetic Algorithm on MIMD Machines. In: Davidor, Y., Männer, R., Schwefel, H.-P. (eds.) PPSN 1994. LNCS, vol. 866, pp. 504–513. Springer, Heidelberg (1994)

    Google Scholar 

  10. Nowostawski, M., Poli, R.: A Highly Scalable Parallel Genetic Algorithm Based on Dynamic Deme Reorganization, Technical Report:CSRP-99-12, The University of Otago, New Zeeland (1999)

    Google Scholar 

  11. Lin, S.-H., Goodman, E.D., Punch, W.F.: Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problem. In: Angeline, P., Renolds, R., Eberhart, R. (eds.) Sixth International Conference on Evolutionary Programming, pp. 383–393. Springer, Heidelberg (1997)

    Chapter  Google Scholar 

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

  13. Kim, H.S., Cho, S.B.: An Efficient genetic algorithm with less fitness valuations by clustering. In: Proc. of the 2001 IEEE Congress on Evolutionary Computation, Seoul, Korea, May 27–30, pp. 887–894 (2001)

    Google Scholar 

  14. Web-site: http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/tsp/

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© 2003 Springer-Verlag Berlin Heidelberg

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Acan, A. (2003). A Selectionless Two-Society Multiple-Deme Approach for Parallel Genetic Algorithms. In: Yazıcı, A., Şener, C. (eds) Computer and Information Sciences - ISCIS 2003. ISCIS 2003. Lecture Notes in Computer Science, vol 2869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39737-3_120

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  • DOI: https://doi.org/10.1007/978-3-540-39737-3_120

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20409-1

  • Online ISBN: 978-3-540-39737-3

  • eBook Packages: Springer Book Archive

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