Genetic algorithms with multi-parent recombination

  • A. E. Eiben
  • P. -E. Raué
  • Zs. Ruttkay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 866)


We investigate genetic algorithms where more than two parents are involved in the recombination operation. We introduce two multi-parent recombination mechanisms: gene scanning and diagonal crossover that generalize uniform, respecively n-point crossovers. In this paper we concentrate on the gene scanning mechanism and we perform extensive tests to observe the effect of different numbers of parents on the performance of the GA. We consider different problem types, such as numerical optimization, constrained optimization (TSP) and constraint satisfaction (graph coloring). The experiments show that 2-parent recombination is inferior on the classical DeJong functions. For the other problems the results are not conclusive, in some cases 2 parents are optimal, while in some others more parents are better.


Genetic Algorithm Travelling Salesman Problem Constraint Satisfaction Problem Graph Coloring Recombination Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. [Bäc93]
    T.-B. Bäck and H.-P. Schwefel, An Overview of Evolutionary Algorithms for parameter Optimization, Journal of Evolutionary Computation 1(1), 1993, pp. 1–23.Google Scholar
  2. [Che91]
    P. Cheeseman, B. Kenefsky and W.M. Taylor, Where the really hard problems are, Proc. of IJCAI-91, 1991, pp. 331–337.Google Scholar
  3. [Cor93]
    A.L. Corcoran and R.L. Wainwright, LibGA: A User Friendly Workbench for Order-Based Genetic Algorithm Research, Proc. of Applied Computing: Sates of the Art and Practice, 1993, pp. 111–117.Google Scholar
  4. [DavY91]
    Y. Davidor, Epistasis Variance: A View-point on GA-Hardness, Proc. of FOGA-90, 1991, pp. 23–35.Google Scholar
  5. [DavL91]
    L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991.Google Scholar
  6. [Eib91]
    A.E. Eiben, A Method for Designing Decision Support Systems for Operational Planning, PhD Thesis, Eindhoven University of Technology, 1991.Google Scholar
  7. [Eib94a]
    A.E. Eiben, P.-E. Raué and Zs. Ruttkay, Solving Constraint Satisfaction Problems Using Genetic Algorithms, proceedings of the 1st IEEE World Conference on Computational Intelligence, to appear in 1994.Google Scholar
  8. [Eib94b]
    A.E. Eiben, P-E., Raué, Zs. Ruttkay, GA-easy and GA-hard Constraint Satisfaction Problems, Proc. of the ECAI'94 Workshop on Constraint Processing, LNCS Series, Springer-Verlag, to appear in August, 1994.Google Scholar
  9. [Gol89]
    D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989.Google Scholar
  10. [Gre85]
    J. Grefenstette, R. Gopal, B. Rosmaita and D. Van Gucht, Genetic Algorithms for the Travelling Salesman Problem, Proc. of ICGA-85, 1985, pp.160–168.Google Scholar
  11. [Mic92]
    Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1992.Google Scholar
  12. [Müh89]
    H. Mühlenbein, Parallel Genetic Algorithms, Population Genetics and Combinatorial Optimization, Proc. of ICGA-89, 1989, pp. 416–421.Google Scholar
  13. [Oli87]
    I.M. Oliver, D.J. Smith and J.R.C. Holland, A study of permutation crossover operators on the travelling salesman problem, Proc. of ICGA-87, 1987, pp. 224–230.Google Scholar
  14. [Ser92]
    G. Seront and H. Bersini, In Search of a Good Evolution-Optimization Crossover, Proc. of PPSN 2, 1992, pp. 479–488.Google Scholar
  15. [Whi91]
    D. Whitley, T. Starkweather and D. Shaner, The traveling salesman and sequence scheduling: quality solutions using genetic edge recombination, In: [DavL91],, pp. 350–372.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • A. E. Eiben
    • 1
  • P. -E. Raué
    • 1
  • Zs. Ruttkay
    • 1
  1. 1.Artificial Intelligence Group Dept. of Mathematics and Computer ScienceVrije Universiteit AmsterdamAmsterdam

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