An Enhanced Genetic Algorithm Integrated with Orthogonal Design

  • Kit Yan Chan
  • C. K. Kwong
  • Tharam S. Dillon
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 403)


Chapter 9 introduced an innovative computational intelligence method based on simulated annealing, to perform optimization of new products. In this chapter, we introduce another computational intelligence method known as evolutionary algorithms to perform optimization of new products.


Genetic Algorithm Evolutionary Algorithm Orthogonal Array Recycle Concrete Aggregate Orthogonal Design 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Box, G.E.P., Hunter, W.G., Hunter, J.S.: Statistics for Experimenters. John Wiley (1978)Google Scholar
  2. Bai, H., Kwong, C.K.: Inexact genetic algorithm approach to target values setting of engineering requirements in QFD. International Journal of Production Research 41(16), 3861–3881 (2003)zbMATHCrossRefGoogle Scholar
  3. Baker, J.E.: Adaptive selection methods for genetic algorithms. In: Proceedings of the First International Conference on Genetic Algorithms, pp. 101–111 (1985)Google Scholar
  4. Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms, pp. 14–21 (1987)Google Scholar
  5. Bonissone, P.P., Subbu, R., Eklund, N., Kiehl, T.R.: Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Transactions on Evolutionary Computation 10(3), 256–280 (2006)CrossRefGoogle Scholar
  6. Chan, K.Y., Emin Aydin, M., Fogarty, T.C.: A Taguchi method-based crossover operator for the parametrical problems. In: Proceedings of the IEEE International Congress on Evolutionary Computation, pp. 971–977 (2003)Google Scholar
  7. Chan, K.Y., Kwong, C.K., Jiang, H., Aydin, M.E., Fogarty, T.C.: A new orthogonal array based crossover, with analysis of gene interactions, for evolutionary algorithms and its application to car door design. Expert Systems Applications 37(5), 3853–3862 (2010)CrossRefGoogle Scholar
  8. Chipperfield, A.J., Fleming, P.J., Fonseca, C.M.: Genetic Algorithm Tools for Control Systems Engineering. In: Proceedings of Adaptive Computing in Engineering Design and Control, pp. 128–133 (1994)Google Scholar
  9. Chipperfield, A.J., Fleming, P.J.: The MATLAB genetic algorithm toolbox. In: Proceedings of the IEE Colloquium on Applied Control Techniques using MATLAB, pp. 10/1–10/4 (1995)Google Scholar
  10. Cvetkovic, D., Muhlenbein, H.: The optimal population size for uniform crossover and truncation selection, in Technical Report GMD-AS-TR-94-11, St Augustine, Germany (1994)Google Scholar
  11. Davidor, Y.: Epistasis variance: a viewpoint on GA-hardness. In: Rawlins, G.J.E. (ed.) Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo (1991)Google Scholar
  12. Dimopoulos, C., Zalzala, A.M.S.: Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons. IEEE Transactions on Evolutionary Computation 4(2), 93–113 (2000)CrossRefGoogle Scholar
  13. Davision, E.J.: Benchmark problems for control system design. International Federation of Automatic Control (May 1990)Google Scholar
  14. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley Longman, Inc., United States of America (1989)zbMATHGoogle Scholar
  15. Ho, S.Y., Shu, L.S., Chen, H.M.: Intelligent genetic algorithm with a new intelligent crossover using orthogonal arrays. In: Proceedings of the Genetic and Evolutionary Computation Conference, vol. 1, pp. 289–296 (1999)Google Scholar
  16. Ho, S.Y., Shu, L.S., Chen, J.H.: Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Transactions on Evolutionary Computation 8(6), 522–541 (2004)CrossRefGoogle Scholar
  17. Ho, S.Y., Chen, H.M., Ho, S.J., Chen, T.K.: Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space. IEEE Transactions on Systems, Man and Cybernetics –Part B: Cybernetics 34(2), 1031–1044 (2004)CrossRefGoogle Scholar
  18. Ho, S.Y., Chen, J.H., Huang, M.H.: Inheritable genetic algorithm for bi-objective 0/1 combinatorial optimization problems and it applications. IEEE Transactions on Systems, Man and Cybernetics –Part B: Cybernetics 34(1), 609–620 (2004)CrossRefGoogle Scholar
  19. Ho, S.J., Ho, S.Y., Hung, M.H., Shu, L.S., Huang, H.L.: Designing structure-specified mixed H2/H¥ optimal controllers using an intelligent genetic algorithm IGA. IEEE Transactions on Control Systems Technology 13(6), 1119–1124 (2005)CrossRefGoogle Scholar
  20. Ho, S.Y., Chen, H.M.: A GA-based systematic reasoning approach for solving traveling salesman problems using an orthogonal array crossover. In: Proceeding of the Fourth International Conference on High Performance Computing in the Asia Pacific Region, vol. 2, pp. 659–663 (2000)Google Scholar
  21. Ho, S.Y., Chen, H.M.: An efficient evolutionary algorithm for accurate polygonal approximation. Pattern Recognition 34, 2305–2317 (2003)CrossRefGoogle Scholar
  22. Huang, H.L., Ho, S.Y.: Mesh optimization for surface approximation using an efficient coarse-to-fine evolutionary algorithm. Pattern Recognition 36, 1065–1081 (2003)zbMATHCrossRefGoogle Scholar
  23. KrishnaKumar, K., Narayanaswamy, S., Garg, S.: Solving large parameter optimization problems using a genetic algorithm with stochastic coding. In: Winter, G., Périaux, J., Galán, M., Cuesta, P. (eds.) Genetic Algorithms in Engineering and Computer Science. Wiley, New York (1995)Google Scholar
  24. Kwong, C.K., Chan, K.Y., Aydin, M.E., Fogarty, T.C.: An orthogonal array based genetic algorithm for developing neural network based process models of fluid dispensing. International Journal of Production Research 44(12), 4815–4836 (2006)zbMATHCrossRefGoogle Scholar
  25. Khuri, A.I., Cornell, J.A.: Response Surfaces Design and Analysis. Marcel Dekker, Inc., New York (1996)Google Scholar
  26. Kim, J.D., Choi, M.S.: Stochastic approach to experimental analysis of cylindrical lapping process. International Journal of Machines Tools Manufacturing 35(1), 51–59 (1995)CrossRefGoogle Scholar
  27. Kim, K., Moskowitz, H., Dhingra, A., Evans, G.: Fuzzy multicriteria models for quality function deployment. European Journal of Operational Research 121, 504–518 (2000)zbMATHCrossRefGoogle Scholar
  28. Leung, Y.W., Wang, Y.: Multiobjective programming using uniform design and genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 30(3), 293–304 (2000)CrossRefGoogle Scholar
  29. Leung, Y.W., Wang, Y.: An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation 5(1), 41–53 (2001)CrossRefGoogle Scholar
  30. Lin, Y.H., Tyan, Y.Y., Chang, T.P., Chang, C.Y.: An assessment of optimal mixture for concrete made with recycled concrete aggregates. Cement and Concrete Research 34, 1373–1380 (2004)CrossRefGoogle Scholar
  31. Mohan, N.S., Ramachandra, A., Kulkarni, S.M.: Influence of process parameters on cutting force and torque during drilling of glass fiber polyester reinforced composites. Composite Structures 71, 407–413 (2005)CrossRefGoogle Scholar
  32. Montgomery, D.C.: Design and Analysis of Experiments. John Wiley and Sons, Inc., New York (1997)zbMATHGoogle Scholar
  33. Muhlenbein, H.: How genetic algorithms really work - Part I: Mutation and hill climbing. In: Proceedings of the 2nd International Conference on Parallel Problem Solving from Nature, pp. 15–25 (1992)Google Scholar
  34. Phadke, M.S.: Quality engineering using robust design. Prentice Hall, New York (1987)Google Scholar
  35. Reeves, C.R.: Predictive measures for problem difficulty. In: Proceedings of the 1999 Congress on Evolutionary Computation, vol. 1, pp. 736–742 (1999)Google Scholar
  36. Taguchi, G., Konishi, S.: Orthogonal Arrays and Linear Graphs. American Supplier Institute, Dearborn (1987)Google Scholar
  37. Tsai, J.T., Liu, T.K., Chou, J.H.: Hybrid Taguchi-genetic algorithm for global numerical optimization. IEEE Transactions on Evolutionary Computation 8(4), 365–377 (2004)CrossRefGoogle Scholar
  38. Unal, R., Stanley, D.O., Joyner, C.R.: Propulsion system design optimization using the Taguchi Method. IEEE Transactions on Engineering Management 40(3), 315–322 (1993)CrossRefGoogle Scholar
  39. Whitley, D.: The genitor algorithm and selective pressure: why rank-based allocation of reproductive trials is best. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 116–121 (1989)Google Scholar
  40. Whitley, D., Mathias, K., Rana, S., Dzubera, J.: Building better test function. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 239–246 (1995)Google Scholar
  41. Yao, X., Lin, Y., Lin, G.: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3(2), 82–102 (1999)MathSciNetCrossRefGoogle Scholar
  42. Zhang, Q., Leung, Y.W.: An orthogonal genetic algorithm for multimedia multicast routing. IEEE Transactions on Evolutionary Computation 3(1), 53–62 (1999)CrossRefGoogle Scholar
  43. Zimmermann, H.J.: Fuzzy Set Theory and Its Applications, 3rd edn. Kluwer, Boston (1996)zbMATHGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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