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An Endosymbiotic Evolutionary Algorithm for Optimization

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Abstract

This paper proposes a new symbiotic evolutionary algorithm to solve complex optimization problems. This algorithm imitates the natural evolution process of endosymbionts, which is called endosymbiotic evolutionary algorithm. Existing symbiotic algorithms take the strategy that the evolution of symbionts is separated from the host. In the natural world, prokaryotic cells that are originally independent organisms are combined into an eukaryotic cell. The basic idea of the proposed algorithm is the incorporation of the evolution of the eukaryotic cells into the existing symbiotic algorithms. In the proposed algorithm, the formation and evolution of the endosymbionts is based on fitness, as it can increase the adaptability of the individuals and the search efficiency. In addition, a localized coevolutionary strategy is employed to maintain the population diversity. Experimental results demonstrate that the proposed algorithm is a promising approach to solving complex problems that are composed of multiple sub- problems interrelated with each other.

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References

  1. D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley: Massachusetts, 1989.

    Google Scholar 

  2. M.A. Potter, “The design and analysis of a computational model of cooperative coevolution,” Ph.D. dissertation, George Mason University, 1997.

  3. D.E. Moriarty and R. Miikkulainen, “Forming neural networks through efficient and adaptive coevolution,” Evolutionary Computation, vol. 5, pp. 373-399, 1997.

    Google Scholar 

  4. L. Bull and T.C. Fogarty, “Artificial symbiogenesis,” Artificial Life, vol. 2, pp. 269-292, 1995.

    Google Scholar 

  5. W.D. Hillis, “Co-evolving parasites improve simulated evolution as an optimization procedure,” Physica D, vol. 42, pp. 228-234, 1990.

    Google Scholar 

  6. C.D. Rosin and R.K. Belew, “New methods for competitive coevolution,” Evolutionary Computation, vol. 5, pp. 1-29, 1997.

    Google Scholar 

  7. S. Nolfi and D. Floreano, “Coevolving predator and prey robots: Do arms races arise in artificial evolution,” Artificial Life, vol. 4, pp. 311-335, 1998.

    Google Scholar 

  8. M.L. Maher and J. Poon, “Modelling design exploration as co-evolution,” Microcomputers in Civil Engineering, vol. 11, pp. 195-210, 1996.

    Google Scholar 

  9. L. Bull, “Evolutionary computing in multi-agent environments: Partners,” Proc. 7th International on Conference Genetic Algorithms, East Lansing, MI, pp. 370-377, 1997.

    Google Scholar 

  10. L. Margulis, Symbiosis in Cell Evolution, W.H. Freeman, San Francisco, 1981.

    Google Scholar 

  11. J. Lovelock and L. Margulis, “Dr. Lynn Margulis: Microbiological collaboration of Gaia,” http://www.magan.com.au/ ~prfbrown/gaia lyn.html, Mountain Man Graphics, Australia, 1996.

    Google Scholar 

  12. N.A. Campbell, L.G. Mitchell, and J.B. Reece, Biology: Concepts & Connections, 2nd ed., Benjamin/Cummings Publishing Company Inc., Redwood City, CA, 1996.

    Google Scholar 

  13. Y.K. Kim, J.Y. Kim, and Y. Kim, “Analysis of partnering strategies in symbiotic evolutionary algorithms,” Working Paper, Department of Industrial Engineering, Chonnam National University, Korea, 1999.

    Google Scholar 

  14. G. Syswerda, “A study of reproduction in generational and steady-state genetic algorithms,” Foundations of Genetic Algorithms, edited by Gregory J.E. Rawlins, San Mateo, CA, pp. 94-101, 1991.

    Google Scholar 

  15. S.A. Kauffman and S. Johnsen, “Co-evolution to the edge of chaos: Coupled fitness landscapes, poised states and coevolutionary avalanches,” Artificial Life II: Proc. of an Interdisciplinary Workshop on the Synthesis and Simulation of Living Systems, Redwood City, CA, pp. 325-370, 1989.

  16. S.A. Kauffman, The Origins of Order: Self-Organization and Selection in Evolution, Oxford University Press, New York, Oxford, 1993.

    Google Scholar 

  17. Y.K. Kim, J.Y. Kim, and Y. Kim, “A coevolutionary algorithm for balancing and sequencing in mixed model assembly lines,” Applied Intelligence, vol. 13, pp. 247-258, 2000.

    Google Scholar 

  18. Y. Davidor, “A naturally occurring niche and species phenomenon: The model and first results,” Proc. 4th International on Conference Genetic Algorithms, San Mateo, CA, pp. 257-263, 1991.

  19. H.J.C. Barbosa, “A coevolutionary genetic algorithm for a game approach to structural optimization,” Proc. 7th International on Conference Genetic Algorithms, East Lansing, MI, pp. 545-552, 1997.

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Kim, J.Y., Kim, Y. & Kim, Y.K. An Endosymbiotic Evolutionary Algorithm for Optimization. Applied Intelligence 15, 117–130 (2001). https://doi.org/10.1023/A:1011279221489

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