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<META name=vsisbn content="0849398010">
<META name=vstitle content="Industrial Applications of Genetic Algorithms">
<META name=vsauthor content="Charles Karr; L. Michael Freeman">
<META name=vsimprint content="CRC Press">
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<META name=vspubdate content="12/01/98">
<META name=vscategory content="Web and Software Development: Artificial Intelligence: Other">
<TITLE>Industrial Applications of Genetic Algorithms:What Can I Do with a Learning Classifier System?</TITLE>
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<P><FONT SIZE="+1"><B>ACKNOWLEDGMENTS</B></FONT></P>
<P>The authors wish to acknowledge the financial support from the National Science Foundation (grant ECS-9212066) and from NASA’s Graduate Student Researchers Program (contract NGT 4-52403).
</P>
<P>The authors thank the editors for their invitation to be included in this project. Much insight was gained during the GA course at The University of Alabama and they have provided a forum to showcase the students’ projects.</P>
<P>Lastly, the first author acknowledges his family’s great support while staying in school longer than most students. Without their support, life would be “not so grand” and graduate school would probably have taken its toll long ago.</P>
<P><FONT SIZE="+1"><B>REFERENCES</B></FONT></P>
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<DD><B>16</B> Rummery, G. A. & Niranjan, M. (1994) On-line Q-learning using connectionist systems. Cambridge University Engineering Department (Report No. CUED/F-INFENG/TR 166). Cambridge CB2 1PZ, England.
<DD><B>17</B> Smith, S.F. (1980) A learning system based on genetic adaptive algorithms. Unpublished Doctoral Dissertation. University of Pittsburgh.
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<DD><B>20</B> Smith, R.E., Forrest, S. & Perelson, A.S. (1993) Searching for diverse cooperative populations with genetic algorithms. <I>Evolutionary Computation</I>, (1)3, pp. 127-149.
<DD><B>21</B> Smith, R.E. & Cribbs, H.B. (1994) Is a learning classifier system a type of neural network? <I>Evolutionary Computation</I>, (2) 1, pp. 19-36.
<DD><B>22</B> Smith, R.E. & Cribbs, H.B. (1996) Cooperative versus competitive system elements in coevolutionary systems. <I>FROM ANIMALS TO ANIMATS 4: Proceedings of the 4th International Conference on Simulation of Adaptive Behavior</I>.
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<DD><B>24</B> Valenzuela-Rendon, M. (1991) The Fuzzy classifier system: A classifier system for continuously varying variables. <I>Proceedings of the Fourth International Conference on Genetic Algorithms</I>, pp. 346-353.
<DD><B>25</B> Watkins, J. C. H. (1989) Learning with delayed rewards. Unpublished doctoral dissertation. King’s College, London.
<DD><B>26</B> Watkins, J.C.H. & Dayan, P. (1992) Technical Note: Q-learning. <I>Machine Learning</I> <B>8</B>, pp. 279-292.
<DD><B>27</B> Whitley, D. (1989) Analysis of the GENITOR Algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. <I>Proceedings of the Third International Conference on Genetic Algorithms</I>. Morgan Kaufmann: San Mateo, CA.
<DD><B>28</B> Wilson, S.W. (1986) Classifier systems and the animat problem. The Rowland Institute of Science (Research Memo RIS No. 36r). Cambridge, MA.
<DD><B>29</B> Wilson, S. W. (1990) Perceptron Redux: Emergence of structure. In S. Forrest (ed.) <I>Emergent Computation: Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-Organization, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks</I>. pp. 249-256. North-Holland: Amsterdam.
<DD><B>30</B> Wilson, S.W. (1994) ZCS: A zeroth level classifier system. <I>Evolutionary Computation</I>, (2) 1, pp. 1-18.
<DD><B>31</B> Wilson, S.W. (1995) Classifier fitness based on accuracy. <I>Evolutionary Computation</I>, (3) 2, pp. 149-175.
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