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Introduction to Evolutionary Computing in System Design

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Advances in Evolutionary Computing for System Design

Part of the book series: Studies in Computational Intelligence ((SCI,volume 66))

In this chapter, an introduction on the use of evolutionary computing techniques, which are considered as global optimization and search techniques inspired from biological evolutions, in the domain of system design is presented. A variety of evolutionary computing techniques are first explained, and the motivations of using evolutionary computing techniques in tackling system design tasks are then discussed. In addition, a number of successful applications of evolutionary computing to system design tasks are described.

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References

  1. Dumitrescu, D., Lazzerini, B., Jain, L.C., Dumitrescu, A., (2000), Evolutionary Computation, CRC Press, Boca Raton, FL, USA.

    MATH  Google Scholar 

  2. Suzuki, R., Arita, T., (2007), The dynamic changes in roles of learning through the Baldwin effect, Artificial Life, 13, 31-43.

    Article  Google Scholar 

  3. http://en.wikipedia.org/wiki/System

  4. Holland, J.H., (1962), Outline for a logical theory of adaptive systems, J. ACM, 3,297-314.

    Article  Google Scholar 

  5. Fogel, L.J., Owens, A.J., Walsh, M.J., (1966), Artificial Intelligence through Simulated Evolution, John Wiley, New York.

    MATH  Google Scholar 

  6. Rechenberg, I., (1994), Evolutionary strategy, Computational Intelligence: Imi- tating Life, Zurada, J.M., Marks II, R., Robinson, C., (Eds.), IEEE Press, 147-159.

    Google Scholar 

  7. Schwefel, H.-P., (1981), Numerical Optimization of Computer Models, John Wiley, Chichester, UK.

    MATH  Google Scholar 

  8. Koza, J.R., (1992), Genetic Programming, MIT Press, Cambridge, MA.

    MATH  Google Scholar 

  9. Holland, J.H., (1986), Escaping brittleness: The possibility of general-purpose learning algorithms applied to parallel rule-based systems, Mach. Learn., 2, Michalski, R.S., Carbonell, J.G., Mitchell, T.M., (Eds.), Morgan Kaufmann, Los Altos, CA, 593-624.

    Google Scholar 

  10. Fogel, D.B. (1997), The advantages of evolutionary computation, BioComputing and Emergent Computation, Lundh, D., Olsson, B., Narayanan A., (Eds.), Skve, Sweden, World Scientific Press, Singapore, 1-11.

    Google Scholar 

  11. Lee, L.H., Lee, C.U., Tan, Y.P., (2007), A multi-objective genetic algorithm for robust flight scheduling using simulation, Eur. J. Oper. Res., 177, 1948-1968.

    Article  MATH  MathSciNet  Google Scholar 

  12. Jozefowska, J., Mika, M., Rozycki, R., Waligora, G., Weglarz, J., (2002), A heuristic approach to allocating the continuous resource in discrete-continuous scheduling problems to minimize the makespan, J. Scheduling, 5, 487-499.

    Article  MATH  MathSciNet  Google Scholar 

  13. Greiner, H., (1996), Robust optical coating design with evolution strategies, Appl. Opt., 35, 5477-5483.

    Article  Google Scholar 

  14. Wiesmann, D., Hammel, U., Bäck, T., (1998), Robust design of multiplayer optical coatings by means of evolutionary algorithms, IEEE Trans. Evol. Comput., 2,162-167.

    Article  Google Scholar 

  15. Alba, E., Dorronsoro, B., Luna, F., Nebro, A.J., Bouvry, P., Hogie, L., (2007), A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs, Comput. Commun., 30, 685-697.

    Article  Google Scholar 

  16. Celso, C. Ribeiro, C.C., Martins, S.L., Rosseti, I., (2007), Metaheuristics for optimization problems in computer communications, Comput. Commun., 30, 656-669.

    Article  Google Scholar 

  17. Han, S.-J., Cho S.-B., (2006), Evolutionary neural networks for anomaly detection based on the behavior of a program, IEEE Trans. Syst., Man Cybernet.— Part B, 36, 559-570.

    Google Scholar 

  18. Fieldsend, J.E., Singh, S., (2005), Pareto Evolutionary Neural Networks, IEEE Trans. Neural Netw.,16, 338-354.

    Article  Google Scholar 

  19. Bonissone, P.P., Chen, Y.-T., Goebel, K., Khedkar, P.S, (1999), Hybrid soft computing systems: industrial and commercial applications, Proc. IEEE, 87, pp. 1641-1667.

    Article  Google Scholar 

  20. Yao, X., (1999), Evolving artificial neural networks, Proc. IEEE, 87, 1423-1447.

    Article  Google Scholar 

  21. Vonk, E., Jain, L.C., and Johnson, R.P., (1997), Automatic Generation of Neural Networks Architecture Using Evolutionary Computing, World Scientific Publishing Company, Singapore.

    Google Scholar 

  22. Van Rooij, A., Jain, L.C., and Johnson, R.P., (1996), Neural Network Training Using Genetic Algorithms, World Scientific Publishing Company, Singapore.

    Google Scholar 

  23. Bonissone, P.P., Subbu, R., Eklund, N., Kiehl, T.R., (2006), Evolutionary algorithms + domain knowledge = real-world evolutionary computation, IEEE Trans. Evol. Comput., 10, 256-280.

    Article  Google Scholar 

  24. Hoffmann, F., (2001), Evolutionary algorithms for fuzzy control system design, Proc. IEEE, 89, 1318-1333.

    Article  Google Scholar 

  25. Lin, C.J., Xu, Y.-J., (2006), A self-adaptive neural fuzzy network with groupbased symbiotic evolution and its prediction applications, Fuzzy Set Syst., 157, 1036-1056.

    Article  MATH  MathSciNet  Google Scholar 

  26. Oh, S.-K., Pedrycz, W., (2005), A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization, Phys. Lett. A, 345, 88-100.

    Article  MATH  Google Scholar 

  27. Jain, L.C. and Martin, N.M. (Eds), (1999), Fusion of Neural Networks, Fuzzy Logic and Evolutionary Computing and their Applications, CRC Press USA.

    Google Scholar 

  28. Paul, C., Valero-Cuevas, F.J., Lipson, H., 2006, Design and Control of Tensegrity Robots for Locomotion, IEEE Trans. Robot., 22, 944-957.

    Article  Google Scholar 

  29. Krasny, D.P., Orin, D.E., (2004), Generating High-Speed Dynamic Running Gaits in a Quadruped Robot Using an Evolutionary Search, IEEE Trans. Syst., Man Cybernet.—Part B, 34, 1685-1696.

    Article  Google Scholar 

  30. Reil, T., Husbands, P., (2002), Evolution of Central Pattern Generators for Bipedal Walking in a Real-Time Physics Environment, IEEE Trans. Evol. Comput., 6, 159-168.

    Article  Google Scholar 

  31. Kodjabachian, J., Meyer, J.-A., (1998), Evolution and development of neural networks controlling locomotion, gradient following and obstacle avoidance in artificial insects, IEEE Trans. Neural Netw., 9, 796-812.

    Article  Google Scholar 

  32. Gallagher, J., Beer, R., Espenschiel, K., Quinn, R., (1996), Application of evolved locomotion controllers to a hexapod robot, Robot. Autonomous Syst., 19,95-103.

    Article  Google Scholar 

  33. Hartmann, M., Haddow, P.C. (2004), Evolution of fault-tolerant and noiserobust digital designs, Proc. Inst. Elect Eng.—Comput. Digit. Tech., 151, 287-294.

    Article  Google Scholar 

  34. Higuchi, T., Iwata, M., Keymeulen, D., Sakanashi, H., Murakawa, H., Iajitani, I., Takahashi, E., Toda, K.,Salami, M., Kajihara, N., Oesu, N., (1999), Real-world applications of analog and digital evolvable hardware, IEEE Trans. Evol. Comput., 220-235.

    Google Scholar 

  35. Lohn, J.D., (1999), Experiments on evolving software models of analog circuits, Commun. ACM, 42, 67-69.

    Article  Google Scholar 

  36. Keymeulen, D., Zebulum, R.S., Jin, Y., Stoica, A., (2000), Fault-tolerant evolvable hardware using field-programmable transistor arrays, IEEE Trans. Rel., 49, 305-316.

    Article  Google Scholar 

  37. Hum, S.V., Okoniewski, M., Davies, R.J., (2005), An evolvable antenna platform based on reconfigurable reflect arrays, Proc. NASA/DoD Conf. Evolvable Hardware, Washington, DC, 139-146.

    Google Scholar 

  38. Lohn, J.D., Hornby, G.S., (2006), Evolvable hardware: using evolutionary computation to design and optimize hardware systems, IEEE Computational Intelligence Magazine, 1, 19-27.

    Article  Google Scholar 

  39. Terrile, R.J., Aghazarian, H., Ferguson, M.I., Fink, W., Huntsberger, T.L., Keymeulen, D., Klimeck, G., Kordon, M.A., Seungwon, L., Allmen, P.V., (2005), Evolutionary computation technologies for the automated design of space systems, Proc. NASA/DoD Conf. Evolvable Hardware, Washington, DC, 131-138.

    Google Scholar 

  40. Yao, X., Higuchi, T., (1999), Promises and challenges of evolvable hardware, IEEE Trans. Syst., Man Cybernet.—Part C, 29, 87-97.

    Article  Google Scholar 

  41. Ko, M.-S., Kang, T.-W., Hwang, C.-S., (1997), Function optimization using an adaptive crossover operator based on locality, Eng. Appl. Artif. Intell., 10, 519-524.

    Article  Google Scholar 

  42. Alba, E., Tomassini, M., (2002), Parallelism and evolutionary algorithms, IEEE Trans. Evol. Comput., 6, 443-462.

    Article  Google Scholar 

  43. Harik, G., Lobo, F., Goldberg, D., (1999), The compact genetic algorithm, IEEE Trans. Evol. Comput., 3, 287-297.

    Article  Google Scholar 

  44. Miller, J., (1999), An empirical study of the efficiency of learning Boolean functions using a Cartesian genetic programming approach, Proc. Genetic Evol. Comput. Conf., Orlando, FL, 1, 1135-1142.

    Google Scholar 

  45. Tsai, H.-K., Yang, J.-M., Tsai, Y.-F., Kao, C.-Y., (2004), An evolutionary approach for gene expression patterns, IEEE Trans. Inf. Technol. Biol., 8, 69-78.

    Article  Google Scholar 

  46. Hung, C.-M., Huang, Y.-M., Chang, M.-S., (2006), Alignment using genetic programming with causal trees for identification of protein functions, Nonlinear Analysis, 65, 1070-1093.

    Article  MATH  MathSciNet  Google Scholar 

  47. Ngom, A., (2006), Parallel evolution strategy on grids for the protein threading problem, J. Parallel Distrib. Comput., 66, 1489-1502.

    Article  MATH  Google Scholar 

  48. Ghosh, A. and Jain, L.C. (Eds) (2005), Evolutionary Computation in Data Min- ing, Springer, Germany.

    Google Scholar 

  49. Guo, H., Jack, L.B., Nandi, A.K., (2005), Feature generation using genetic programming with application to fault classification, IEEE Trans. Syst., Man Cybernet.—Part B, 35, 89-99.

    Article  Google Scholar 

  50. Toro, F.D., Ros E., Mota, S., Ortega, J., (2006), Evolutionary algorithms for multiobjective and multimodal optimization of diagnostic schemes, IEEE Trans. Bio-Med. Eng., 53, 178-189.

    Article  Google Scholar 

  51. Freitas, H.S., Bojarczuk, A.A., Lopes, C.C., (2000), Genetic programming for knowledge discovery in chest-pain diagnosis, IEEE Eng. Med. Biol. Mag., 19, 38-44.

    Article  Google Scholar 

  52. Gordon, M., Fan W.-G., Pathak, P., (2006), Adaptive web search: evolving a program that finds information, IEEE Intell. Syst., 21, 72-77.

    Article  Google Scholar 

  53. Kuo, R.J., Liao, J.L., Tu, C., (2005), Integration of ART2 neural network and genetic K-means algorithm for analyzing web browsing paths in electronic commerce, Decis. Support Syst., 40, 355-374.

    Article  Google Scholar 

  54. Bode’n, M., Bode’n, M., (2007), Evolving spelling exercises to suit individual student needs, Applied Soft Computing, 7, 126-135.

    Article  Google Scholar 

  55. Jain, L.C. (Ed.), (2000), Evolution of Engineering and Information Systems, CRC Press USA.

    Google Scholar 

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Author information

Authors and Affiliations

  1. KES Centre, University of South Australia, Adelaide, Mawson Lakes Campus, 5095, South Australia, Australia

    Prof. Lakhmi C. Jain

  2. Faculty of Information Science and Technology, Multimedia University, Malaysia

    Shing Chiang Tan

  3. School of Electrical and Electronic Engineering, University of Science, Malaysia

    Chee Peng Lim

Authors
  1. Prof. Lakhmi C. Jain
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  2. Shing Chiang Tan
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  3. Chee Peng Lim
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Editor information

Editors and Affiliations

  1. University of South Australia, Adelaide, Mawson Lakes Campus, 5095, South Australia, Australia

    Lakhmi C. Jain

  2. Computing Laboratory, Wolfson Building Parks Road, OX1 3QD, Oxford, England

    Vasile Palade

  3. National University of Singapore, Block E4-06-08, 4 Engineering Drive 3, 117576, Singapore

    Dipti Srinivasan

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© 2007 Springer-Verlag Berlin Heidelberg

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Jain, L.C., Tan, S.C., Lim, C.P. (2007). Introduction to Evolutionary Computing in System Design. In: Jain, L.C., Palade, V., Srinivasan, D. (eds) Advances in Evolutionary Computing for System Design. Studies in Computational Intelligence, vol 66. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72377-6_1

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  • DOI: https://doi.org/10.1007/978-3-540-72377-6_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72376-9

  • Online ISBN: 978-3-540-72377-6

  • eBook Packages: EngineeringEngineering (R0)

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