Investigation into Mutation Operators for Microbial Genetic Algorithm

Samreen Umer

Abstract

Microbial Genetic Algorithm (MGA) is a simple variant of genetic algorithm and is inspired by bacterial conjugation for evolution. In this paper we have discussed and analyzed variants of this less exploited algorithm on known benchmark testing functions to suggest a suitable choice of mutation operator. We also proposed a simple adaptive scheme to adjust the impact of mutation according to the diversity in population in a cost effective way. Our investigation suggests that a clever choice of mutation operator can enhance the performance of basic MGA significantly.

References

  1. Al Jadaan, Omar, L. R. and Rao, C. (2008). Improved selection operator for genetic algorithm. Theoretical and Applied Information Technology, (4.4).
  2. Back, T. and Schwefel, H.-P. (1993). An overview of evolutionary algorithms for parameter optimization. Evolutionary computation, 1(1):1-23.
  3. Baker, J. E. (1985). Adaptive selection methods for genetic algorithms. In Proceedings of an International Conference on Genetic Algorithms and their applications, pages 101-111. Hillsdale, New Jersey.
  4. Fogel, L. J., Owens, A. J., and Walsh, M. J. (1966). Artificial intelligence through simulated evolution.
  5. Goldberg, D. E. and Deb, K. (1991). A comparative analysis of selection schemes used in genetic algorithms. Foundations of genetic algorithms, 1:69-93.
  6. Harvey, I. (2011). The microbial genetic algorithm. In Advances in artificial life. Darwin Meets von Neumann, pages 126-133. Springer.
  7. Hatwagner, F. and Horvath, A. (2012). Maintaining genetic diversity in bacterial evolutionary algorithm. Annales Univ. Sci. Budapest, Sec. Comp, 37:175-194.
  8. John, H. (1992). Holland, adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence.
  9. McCarthy, J. (2007). What is artificial intelligence. URL: http://www-formal. stanford. edu/jmc/whatisai. html, page 38.
  10. Miller, B. L. and Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9(3):193-212.
  11. Ortiz-Boyer, D., Hervás-Martínez, C., and García-Pedrajas, N. (2005). Cixl2: A crossover operator for evolutionary algorithms based on population features. J. Artif. Intell. Res.(JAIR), 24:1-48.
  12. Whitley, L. D. et al. (1989). The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In ICGA, pages 116-123.
  13. Yao, X., Liu, Y., and Lin, G. (1999). Evolutionary programming made faster. Evolutionary Computation, IEEE Transactions on, 3(2):82-102.
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Paper Citation


in Harvard Style

Umer S. (2015). Investigation into Mutation Operators for Microbial Genetic Algorithm . In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA, ISBN 978-989-758-157-1, pages 299-305. DOI: 10.5220/0005614902990305


in Bibtex Style

@conference{ecta15,
author={Samreen Umer},
title={Investigation into Mutation Operators for Microbial Genetic Algorithm},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,},
year={2015},
pages={299-305},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005614902990305},
isbn={978-989-758-157-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 1: ECTA,
TI - Investigation into Mutation Operators for Microbial Genetic Algorithm
SN - 978-989-758-157-1
AU - Umer S.
PY - 2015
SP - 299
EP - 305
DO - 10.5220/0005614902990305