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A Generation Method to Produce GA with GP Capabilities for Signal Modeling

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Multiple Approaches to Intelligent Systems (IEA/AIE 1999)

Abstract

The present work is concerned with how to generate GA chromosomes having the capabilities of GP Chromosomes in signal modeling. Substructure formation genes are used to give the generated GA chromosomes the ability to build complex structures with complex substructures. To avoid any unnecessary structure formation and to save the time of the initial population generation phase, dynamic chromosome constructor is proposed. Hence a completed chromosome never scraped as in the conventional generation methods. Using this method, the generated chromosomes can represent very complicated structures with a relatively simple and direct dealing with the problem instead of dealing with complex programs.

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References

  1. Khajiavi, A.N., Komanduri, R.: Frequency and Time Domain Analyses of Sensor Signals in Drilling-I Correlation with Drill Wear. Int. J. Mach. Tools and Man 35, 775–793 (1995)

    Article  Google Scholar 

  2. Lin, S.C., Yang, R.J.: Force-Based Model For Tool Wear Monitoring In Face Milling. Int. J. Mach. Tools and Man 35, 1201–1211 (1995)

    Article  MathSciNet  Google Scholar 

  3. Bastian, A.: Genetic Programming For Nonlinear Model Identification. Engineering Design and Automation 3, 201–216 (1995)

    Google Scholar 

  4. Koza, J.R.: Genetic Programming. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  5. Langdon, W.B.: Genetic Programming and Data Structures. Kluwer Academic Publishers, Boston (1998)

    MATH  Google Scholar 

  6. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)

    MATH  Google Scholar 

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© 1999 Springer-Verlag Berlin Heidelberg

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Ezzat, A., Inuzuka, N., Itoh, H. (1999). A Generation Method to Produce GA with GP Capabilities for Signal Modeling. In: Imam, I., Kodratoff, Y., El-Dessouki, A., Ali, M. (eds) Multiple Approaches to Intelligent Systems. IEA/AIE 1999. Lecture Notes in Computer Science(), vol 1611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48765-4_15

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  • DOI: https://doi.org/10.1007/978-3-540-48765-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66076-7

  • Online ISBN: 978-3-540-48765-4

  • eBook Packages: Springer Book Archive

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