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ABSTRACT

Spatial based gene selection for division of chromosomes used by crossover operators is proposed for three-dimensional problems. This spatial selection is shown to preserve more genetic material and reduce the disruptive effects of crossover. The disruptive effects of crossover can be quantified by counting the destruction of subgraphs that represent strong linkages between genes. The spatial operator is compared to simple crossover on a practical class of molecular clustering searches. This comparison shows that the spatial crossover significantly out performs simple crossover. Consistent good performance for spatial crossover is demonstrated on the molecular cluster conformation problem [9].

REFERENCES

• 1: Jaroslaw Arabas , Jan J. Mulawka , Jacek Pokrasniewicz, A New Class of the Crossover Operators for the Numerical Optimization, Proceedings of the 6th International Conference on Genetic Algorithms, p.42-48, July 15-19, 1995
• 2: D. Deaven and K. Ho. Molecular geometry optimization with a genetic algorithm. Phy. Rev. Let., 75:288--291, 1995.
• 3: L. J. Eshelman, K. E. Mathias, and J. D. Schaffer. Crossover operator biases: Exploiting the population distribution. Proc. of the 7th ICGA, pages 354--361, 1997.
• 4: David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
• 5: B. Hartke. Application of Evolutionary Computation in Chemistry, volume 110 of Structure and Bonding, chapter Application of Evolutionary Algorithms to global cluster geometry optimization, pages 33--53. Springer, Heidelberg, 2004.
• 6: F. Herrera, M. Lozano, and A. Sånchez. A taxonomy for the crossover operator for real-coded genetic algorithms. an experimental study. International Journal of Intelligent Systems, 18(3):309--338, 2003.
• 7: John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
• 8: I. Hong, A. B. Kahng, and B. R. Moon. Exploiting synergies of multiple crossovers: initial studies. IEEE International Conference on Evolutionary Computation, 1(29):245--250, Nov. Dec. 1995.
• 9: P. Juhas, D. M. Cherba, P. M. Duxbury, W. F. Punch, and S. J. L. Billinge. Ab initio solid state nano-structure determination. Nature, pages 655--658, March 2006.
• 10: Andrew B. Kahng , Byung Ro Moon, Toward More Powerful Recombinations, Proceedings of the 6th International Conference on Genetic Algorithms, p.96-103, July 15-19, 1995
• 11: Z. Michalewicz, G. Nazhiyath, and M. Michalewicz. A note on usefulness of geometrical crossover for numerical optimization problems. Evolutionary Programming, pages 305--312, 1996.
• 12: D.-i. Seo and B. R. Moon. A survey on chromosomal structures and operators for exploiting topological linkages of genes. In GECCO, pages 1357--1368, 2003.
• 13: W. M. Spears and K. A. De Jong. An analysis of multi-point crossover. In G. J. E. Rawlins, editor, Foundations of Genetic Algorithms, pages 301--315, San Mateo, CA, 1991. Morgan Kaufmann.
• 14: H.-S. Yoon and B.-R. Moon. Synergy of multiple crossovers in a genetic algorithm. IEEE Transactions on Evolutionary Computation, 6(2):212 -- 223, April 2002.

INDEX TERMS

Primary Classification:
  F. Theory of Computation
  F.2 ANALYSIS OF ALGORITHMS AND PROBLEM COMPLEXITY
      F.2.2 Nonnumerical Algorithms and Problems
          Subjects: Geometrical problems and computations

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Global optimization

  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING
      Subjects: Physics

Keywords:
algorithm crossover, genetic algorithm, molecular conformation