Line-breeding schemes for combinatorial optimization

  • Rong Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)


Line-breeding is an interesting mating strategy adapted to genetic algorithms. Most experiments on line-breeding have used multimodal functions as a testbed. In this work, we focus on combinatorial optimization problems. We chose the multiple constrained knapsack problem as our testbed. Several line-breeding schemes are explored, from the naive version to more advanced versions with sharing and niching. A new mechanism, dynamic mutation, is also proposed. Under this mechanism, if two individuals are very close to each other, instead of mating, a self-fertilization (i.e., a special mutation) is applied. The experiments presented in this paper show that a line-breeding scheme which uses multiple distanced champions can achieve the best performance. The paper also shows that dynamic mutation can improve not only the quality of solutions but can also identify more local optima.


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  1. 1.
    J. E. Beasley. OR-Library: Distributing Test Problems by Electronic Mail. Journal of the Operational Research Society, (41):1069–1072, 1990. See also html.CrossRefGoogle Scholar
  2. 2.
    P. C. Chu and J. E. Beasley. A Genetic Algorithm for the Multiconstraint Knapsack Problem. Working paper,, The Management School, Imperial College, 1997.Google Scholar
  3. 3.
    Craighurst, R. and Martin, W. Enhancing GA Performance through Crossover Prohibitions Based on Ancestry. In Proceedings of the Sixth International Conference on Genetic Alorithms, pages 130–135, 1995.Google Scholar
  4. 4.
    K. Deb and D. E. Goldberg. An Investigation of Niche and Species Formation in Genetic Function Optimization. In Proceedings of the Third International Conference on Genetic Alorithms, pages 42–50, 1989.Google Scholar
  5. 5.
    L. J. Eshelman and J. D. Schaffer. Preventing Premature Convergence in Genetic Algorithm by Preventing Incest. In Proceedings of the Fourth International Conference on Genetic Alorithms, pages 115–122, 1991.Google Scholar
  6. 6.
    R. B. Hollstien. Artificial Genetic Adaptation in Computer Control System. PhD Dissertation, University of Michigan, 1971.Google Scholar
  7. 7.
    S. Khuri, T. Back, and J. Heitkotter. The Zero-one Multiple Knapsack Problem and Genetic Algorithms. In Proceedings of The ACM Symposium of Applied Computing (SAC94), 1994.Google Scholar
  8. 8.
    B. L. Miller and M. J. Shaw. Genetic Algorithms with Dynamic Niche Sharing for Multimodal Function Optimization. Illigal report no. 95010, University of Illinois at Urbana-Champaign, December 1995.Google Scholar
  9. 9.
    H. Pirkul. A Heuristic Solution Procedure for the Multiconstraint Zero-one Knapsack Problem. Naval Research Logistics, (34):161–172, 1987.MATHGoogle Scholar
  10. 10.
    J. Thiel and S. Voss. Some Experiences on Solving Multiconstraint Zero-one Knapsack Problems with Genetic Algorithms. INFOR, (32):226–242, 1994.MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Rong Yang
    • 1
  1. 1.Department of Computer ScienceUniversity of BristolBristolUK

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