A genetic engineering extension to genetic algorithms

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Gero, JS and Kazakov, V (2001): A genetic engineering extension to genetic algorithms, Evolutionary Computation 9(1): 71-92.




We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.

Extended Abstract


Used References

Alfred V. Aho , John E. Hopcroft, The Design and Analysis of Computer Algorithms, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1974 http://dl.acm.org/citation.cfm?id=578775&CFID=588525319&CFTOKEN=29804931

Anderberg, M. (1973). Cluster Analysis for Applications. Academic Press, New York, New York.

Peter J. Angeline, Genetic programming and emergent intelligence, Advances in genetic programming, MIT Press, Cambridge, MA, 1994 http://dl.acm.org/citation.cfm?id=185992&CFID=588525319&CFTOKEN=29804931

Apostolico, A. (1985). The myriad virtues of subword trees. In Apostolico, A. and Galil, Z., editors, Combinatorial Algorithms on Words, pages 85-96, NATO Advanced Study Institute, Series F: Computer and Systems Sciences, Volume 12, Springer-Verlag, Berlin.

Collins, J. F. and Coulson, A. F. E. (1987). Molecular sequence comparison and alignment. In Nucleic Acid and Protein Sequence Analysis: A Practical Approach, pages 323-358, IRL Press, Washington DC.

Corcoran, A. L. andWainwright, R. L. (1994). Chromosome reduction in Genetic algorithms. Technical Report UTULSA-MCS-94-1, University of Tulsa, Tulsa, Oklahoma.

Maxime Crochemore , Wojciech Rytter, Text algorithms, Oxford University Press, Inc., New York, NY, 1994 http://dl.acm.org/citation.cfm?id=199269&CFID=588525319&CFTOKEN=29804931

Forrest, S. and Mitchell, M. (1993). Relative building-block fitness and building block hypothesis. In Whitley, D., editor, Foundations of Genetic Algorithms, Volume 2, pages 109-126, Morgan Kaufmann, San Mateo, California.

Gero, J. S. and Kazakov, V. (1996). Evolving building blocks for design using genetic engineering: A formal approach. In Gero, J. S., editor, Advances in Formal Design Methods for CAD, pages 31-50, Chapman and Hall, London, England.

David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989 http://dl.acm.org/citation.cfm?id=534133&CFID=588525319&CFTOKEN=29804931

Terry Jones, A description of holland's royal road function, Evolutionary Computation, v.2 n.4, p.409-415, Winter 1994 http://dx.doi.org/10.1162/evco.1994.2.4.409

Kenneth Alan De Jong, An analysis of the behavior of a class of genetic adaptive systems., University of Michigan, Ann Arbor, MI, 1975 http://dl.acm.org/citation.cfm?id=907087&CFID=588525319&CFTOKEN=29804931

Karlin, S., Dembo, A., and Kawabata, T. (1990a). Methods for assessing the statistical significance of molecular sequence features by using general scoring scheme. Proceedings of the National Academy of Science U.S.A., 87:5509-5513.

Karlin, S., Dembo, A., and Kawabata, T. (1990b). Statistical composition of the high-scoring segments from molecular sequences. Annals of Statistics, 18:571-581.

Koza, J. R. (1992). Genetic Programming. Addison-Wesley, Reading, Massachusetts.

Louis, S. J., McGraw, G., andWyckoff, R. O. (1993). CBR Assisted Explanation of GAResults. Journal of Theoretical and Experimental Artificial Intelligence, 5(1):21-28.

Edward M. McCreight, A Space-Economical Suffix Tree Construction Algorithm, Journal of the ACM (JACM), v.23 n.2, p.262-272, April 1976 http://doi.acm.org/10.1145/321941.321946

Needleman, S. B. and Wunsch, C. D. (1970). A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, 48:443-453.

Rosca, J. P. and Ballard, D. H. (1992). Learning by adapting representations in genetic programming. In Cohen,W. and Hirsch, H., editors, Proceedings of the Eleventh International Conference on Machine Learning, pages 407-412, Morgan Kaufmann, San Mateo, California.

Sankoff, D. and Kruskal, J. B., editors (1983). Time Warps, String and Macromolecules: The Theory and Practice of Sequence Comparison. Addison-Wesley, Reading, Massachusetts.

Schuler, G. D., Altschul, S. F., and Lipman, D. J. (1991). A workbench for multiple alignment construction and analysis. PROTEINS: Structure, Function, and Genetics, 9:180-190.

Simon, P. J. (1973). The organization of the complex systems. In Pattee, H. H., editor, Hierarchy Theory: The Challenge of Complex Systems, pages 109-127, G. Braziller, New York. New York.

Weiner, P. (1973). Linear pattern matching algorithms. In Proceedings of the IEEE 14th Annual Symposium on Switching and Automata Theory, pages 1-11, IEEE Press, Piscataway, New Jersey.

Annie S. Wu , Robert K. Lindsay, A comparison of the fixed and floating building block representation in the genetic algorithm, Evolutionary Computation, v.4 n.2, p.169-193, Summer 1996 http://dx.doi.org/10.1162/evco.1996.4.2.169


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