Abstract
Engineered Conditioning (EC) is a Genetic Algorithm operator that works together with the typical genetic algorithm operators: mate selection, crossover, and mutation, in order to improve convergence toward an optimal multiple fault diagnosis. When incorporated within a typical genetic algorithm, the resulting hybrid scheme produces improved reliability by exploiting the global nature of the genetic algorithm as well as “local” improvement capabilities of the Engineered Conditioning operator.
We show the significance of the Engineered Conditioning operator during Multiple Fault Diagnosis (i.e., finding the collection of simultaneously occurring disorders that best explains the observed symptoms or disorder manifestations). Within the Multiple Fault Diagnosis domain, we show the improvement of diagnostic reliability when using the engineered conditioning operator with the genetic algorithm compared to results from the genetic algorithm without the new operator. Reliability is based on the number of diagnostic trials for which the two versions of the genetic algorithm find the optimal diagnosis. For comparison purposes, optimal diagnoses have been computed using a search method that is guaranteed to find the optimal solution.
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References
J. de Kleer and B.C. Williams, “Diagnosing multiple faults,” in Artificial Intelligence, vol. 32, no. 1, pp. 97–130, 1987.
J. de Kleer, A.K. Mackworth, and R. Reiter, “Characterizing diagnoses,” in Proceedings of the 8th National Conference on Artificial Intelligence, vol. 1, pp. 324–330, 1990.
Y. Peng and J.A. Reggia, “A probabilistic causal model for diagnostic problem solving, part I: Integrating symbolie causal inference with numeric probabilistic inference,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-17, no. 2, pp. 146–162, 1987.
R. Reiter, “A theory of diagnosis from first principles,” in Artificial Intelligence, vol. 32, no. I, pp. 57–95, 1987.
R. Davis, “Diagnostic reasoning based on structure and behavior,” in Artificial Intelligence, vol. 24, no. 1–3, pp. 347–410, 1984.
M.R. Genesereth, “The use of design descriptions in automated diagnosis,” in Artificial Intelligence, vol. 24, no. 1–3, pp. 411–436, 1984.
J. Josephson, B. Chandrasekaran, J. Smith, and M. Tanner, “A mechanism for forming composite explanatory hypotheses,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-17, no. 3, pp. 445–454, 1987.
Y. Peng and J.A. Reggia, “A probabilistic causal model for diagnostic problem solving, part II: Diagnostic strategy,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-17, no. 3, pp. 395–406, 1987.
W. D. Potter, B.E. Tonn, M.R. Hilliard, G.E. Liepins, S.L. Purucker, and R.T. Goeltz, “Diagnosis, parsimony, and genetic algorithms,” in Proceedings of the Third Int. Conf. Industrial & Engineering Applications of AI and Expert Systems, pp. 1–8, 1990.
J.A. Reggia, D. Nau, and P. Wang, “Diagnostic expert systems based on a set covering model,” in International Journal of Man-Machine Studies, vol. 19, no. 5, pp. 437–460, 1983.
B. Selman and H.J. Levesque, “Abductive and default reasoning: A computational core,” in Proceedings of the 8th National Conference on Artificial Intelligence, vol. 1, pp. 343–348, 1990.
S. Tuhrim, J. Reggia, and S. Goodall, “An experimental study of criteria for hypothesis plausibility,” in Journal of Experimental and Theoretical Artificial Intelligence, vol. 3, pp. 129–144, 1991.
J.R. Bourne, H.H., Liu, C.D. Orogo, G.C. Collins, N.S. Uckun, and A.J. Brodersen. “Organizing and understanding beliefs in advice-giving diagnostic systems,” in IEEE Transactions on Knowledge and Data Engineering, vol. 3, no. 3, pp. 269–280, 1991.
B. Chandrasekaran, “Models versus rules, deep versus compiled, content versus form,” in IEEE Expert, vol. 6, no. 5, 1991.
B.G. Buchanan and E.H. Shortliffe. Rule-Based Expert Systems, Addison-Wesley: Reading, MA, 1984.
L. Davis, (ed.) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, 1991.
D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Co.: Reading, MA, 1989.
J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor, 1975.
W.D. Potter, J.A. Miller, and O.R. Weyrich, “A comparison of methods for diagnostic decision making,” in Expert Systems with Applications: An International Journal, vol. 1, pp. 425–436, 1990.
G.E. Liepins, M.R. Hilliard, M. Palmer, and M. Morrow, “Greedy genetics,” in Genetic Algorithms and Their Applications: Proc. of the 2nd Int. Conf. on Genetic Algorithms, Lawrence Erlbaum Associates, Publishers: Hillsdale, NJ, 1987.
G.E. Liepins, M.R. Hilliard, J. Richardson, and M. Palmer, “Genetic algorithm applications to set covering and traveling salesman problems,” in OR/AI: The Integration of Problem Solving Strategies, (Brown, ed.), 1990.
J. Edmonds, “Covers and packings in a family of sets,” in Bulletin of the American Mathematics Society, vol. 68, pp. 494–499, 1962.
M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman Publishers: San Francisco, CA, 1979.
S.L. Purucker, et al., “Design and operation of the communication alarm processor expert system,” in Proceedings of the Power Systems and Expert Systems Conference, Seattle, WA, 1989.
P.K. Fink and J.C. Lusth, “Expert systems and diagnostic expertise in the mechanical and electrical domains,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-17, no. 3, pp. 340–349, 1987.
L.J. Holtzblatt, “Diagnosing multiple failures using knowledge of component states,” in Proceedings of the Fourth Conference on Artificial Intelligence Applications, pp. 139–143, San Diego, CA, 1988.
F. Pipitone, “The FIS electronics troubleshooting system,” in IEEE COMPUTER, vol. 19, no. 7, pp. 68–76, 1986.
B. Chandrasekaran and S. Mittal, “Deep versus compiled knowledge approaches to diagnostic problem solving,” in International Journal of Man-Machine Studies, vol. 19, no. 5, pp. 425–436, 1983.
G.E. Liepins and W.D. Potter, “A genetic algorithm approach to multiple fault diagnosis,” in The Genetic Algorithm Handbook, (L. Davis, ed.), Van Nostrand Reinhold: New York, 1991.
M.F. Bramlette and E.E. Bouchard, “Genetic algorithms in parametric design of aircraft,” in [16].
D. Whitley, T. Starkweather, and D. Fuquay, “Scheduling problems and traveling salesmen: The genetic edge recombination operator,” in Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufmann Publishing: San Mateo, CA, 1989.
P. Jog, J.Y. Suh, and D.V. Gucht, “The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem,” in Proceedings of the 3rd International Conference on Genetic Algorithms, Morgan Kaufmann Publishing: San Mateo, CA, 1989.
Y. Peng and J.A. Reggia, “A connectionist model for diagnostic problem solving,” in IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-19, no. 2, pp. 285–298, 1989.
O.R. Weyrich, W.D. Potter, and J.A. Miller, “A simulation study of heuristic techniques for diagnostic decision making,” in Proceedings of the AI and Simulation Conference, 1990 SCS Eastern Multiconference, pp. 217–223, 1990.
R.V. Gandham, “On improving diagnostic decision making,” Master of Science Thesis, University of Georgia, 1991.
E. Charniak and D. McDermott, Introduction to Artificial Intelligence, Addison-Wesley: Reading, MA, 1985.
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Potter, W.D., Miller, J.A., Tonn, B.E. et al. Improving the reliability of heuristic multiple fault diagnosis via the EC-based Genetic Algorithm. Appl Intell 2, 5–23 (1992). https://doi.org/10.1007/BF00058573
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DOI: https://doi.org/10.1007/BF00058573