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Engineering Evolutionary Intelligent Systems pp 1–22Cite as

Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 82))

Summary

Designing intelligent paradigms using evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty and vagueness. In this Chapter, we illustrate the various possibilities for designing intelligent systems using evolutionary algorithms and also present some of the generic evolutionary design architectures that has evolved during the last couple of decades. We also provide a review of some of the recent interesting evolutionary intelligent system frameworks reported in the literature.

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References

  1. Abraham A, Grosan C, Han SY, Gelbukh A (2005) Evolutionary multiobjective optimization approach for evolving ensemble of intelligent paradigms for stock market modeling. In: Alexander Gelbukh et al. (eds.) 4th Mexican international conference on artificial intelligence, Mexico, Lecture notes in computer science, Springer, Berlin Heidelberg New York, pp 673–681

    Google Scholar 

  2. Abraham, A (2004) Meta-learning evolutionary artificial neural networks. Neurocomput J 56c:1–38

    Google Scholar 

  3. Abraham A (2003) i-Miner: A Web Usage Mining Framework Using Hierarchical Intelligent Systems, The IEEE International Conference on Fuzzy Systems, FUZZ-IEEE’03, IEEE Press, ISBN 0780378113, pp 1129–1134

    Google Scholar 

  4. Abraham A, Ramos V (2003), Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming, 2003 IEEE Congress on Evolutionary Computation (CEC2003), Australia, IEEE Press, ISBN 0780378040, pp 1384–1391, 2003

    Google Scholar 

  5. Abraham A (2003), EvoNF: A Framework for Optimization of Fuzzy Inference Systems Using Neural Network Learning and Evolutionary Computation, The 17th IEEE International Symposium on Intelligent Control, ISIC’02, IEEE Press, ISBN 0780376218, pp 327–332

    Google Scholar 

  6. Aouiti C, Alimi AM, Karray F, Maalej A (2005) The design of beta basis function neural network and beta fuzzy systems by a hierarchical genetic algorithm. Fuzzy Sets Syst 154(2):251–274

    Article  MATH  MathSciNet  Google Scholar 

  7. Bhattacharya A, Abraham A, Vasant P, Grosan C (2007) Meta-learning evolutionary artificial neural network for selecting flexible manufacturing systems under disparate level-of-satisfaction of decision maker. Int J Innovative Comput Inf Control 3(1):131–140

    Google Scholar 

  8. Baxter J (1992) The evolution of learning algorithms for artificial neural networks, Complex systems, IOS, Amsterdam, pp 313–326

    Google Scholar 

  9. Bezdek, JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York

    MATH  Google Scholar 

  10. Boers EJW, Borst MV, Sprinkhuizen-Kuyper IG (1995) Artificial neural nets and genetic algorithms. In: Pearson DW et al. (eds.) Proceedings of the international conference in Ales, France, Springer, Berlin Heidelberg New York, pp 333–336

    Google Scholar 

  11. Cai X, Zhang N, Venayagamoorthy GK, Wunsch II DC (2007) Time series prediction with recurrent neural networks trained by a hybrid PSOEA algorithm. Neurocomputing 70(13–15):2342–2353

    Article  Google Scholar 

  12. Castillo PA, Merelo JJ, Arenas MG, Romero G (2007) Comparing evolutionary hybrid systems for design and optimization of multilayer perceptron structure along training parameters. Inf Sci 177(14):2884–2905

    Article  Google Scholar 

  13. Capi G, Doya K (2005), Evolution of recurrent neural controllers using an extended parallel genetic algorithm. Rob Auton Syst 52(2–3):148–159

    Google Scholar 

  14. Chen Y, Yang B, Dong J, Abraham A (2005) Time-series forecasting using flexible neural tree model. Inf Sci 174(3–4):219–235

    Article  MathSciNet  Google Scholar 

  15. Chen Y, Yang B, Abraham A, Peng L (2007) Automatic design of hierarchical takagi-sugeno fuzzy systems using evolutionary algorithms. IEEE Trans Fuzzy Syst 15(3):385–397

    Article  Google Scholar 

  16. Chu B, Kim D, Hong D, Park J, Chung JT, Chung JH, Kim TH (2008) GA-based fuzzy controller design for tunnel ventilation systems, Journal of Automation in Construction, 17(2):130–136

    Article  Google Scholar 

  17. Delgado M, Pegalajar MC (2005) A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference. Pattern Recognit 38(9):1444–1456

    Article  MATH  Google Scholar 

  18. Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of 6th Internationl Symposium on Micro Machine and Human Science, Nagoya, Japan, IEEE Service Center, Piscataaway, NJ, pp 39–43

    Chapter  Google Scholar 

  19. Edwards R, Abraham A, Petrovic-Lazarevic S (2005) Computational intelligence to model the export behaviour of multinational corporation subsidiaries in Malaysia. Int J Am Soc Inf Sci Technol (JASIST) 56(11):1177–1186

    Article  Google Scholar 

  20. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, USA

    MATH  Google Scholar 

  21. Fontanari JF, Meir R (1991) Evolving a learning algorithm for the binary perceptron, Network, vol. 2, pp 353–359

    Google Scholar 

  22. Franke C, Hoffmann F, Lepping J, Schwiegelshohn U (2008) Development of scheduling strategies with Genetic Fuzzy systems, Applied Soft Computing Journal, 8(1):706–721

    Article  Google Scholar 

  23. Frean M (1990), The upstart algorithm: a method for constructing and training feed forward neural networks. Neural Comput 2:198–209

    Article  Google Scholar 

  24. Fullmer B, Miikkulainen R (1992) Using marker-based genetic encoding of neural networks to evolve finite-state behaviour. In: Varela FJ, Bourgine P (eds.) Proceedings of the first European conference on artificial life, France, pp 255–262

    Google Scholar 

  25. Garca-Pedrajas N, Hervs-Martnez C, Muoz-Prez J (2002) Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks). Neural Netw 15(10):1259–1278

    Article  Google Scholar 

  26. Grau F (1992)Genetic synthesis of boolean neural networks with a cell rewriting developmental process. In: Whitely D, Schaffer JD (eds.) Proceedings of the international workshop on combinations of genetic algorithms and neural Networks, IEEE Computer Society Press, CA, pp 55–74

    Chapter  Google Scholar 

  27. Gonzalez A, Herrera F (1997) Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware Soft Comput 4(3)

    Google Scholar 

  28. Grosan C, Abraham A, Nicoara M (2005) Search optimization using hybrid particle sub-swarms and evolutionary algorithms. Int J Simul Syst, Sci Technol UK 6(10–11):60–79

    Google Scholar 

  29. Gutierrez G, Isasi P, Molina JM, Sanchis A, Galvan IM (2001) Evolutionary cellular configurations for designing feedforward neural network architectures, connectionist models of neurons. In: Jose Mira et al. (eds.) Learning processes, and artificial intelligence, Springer, Berlin Heidelberg New York, LNCS 2084, pp 514–521

    Google Scholar 

  30. Harp SA, Samad T, Guha A (1989) Towards the genetic synthesis of neural networks. In: Schaffer JD (ed.) Proceedings of the third international conference on genetic algorithms and their applications, Morgan Kaufmann, CA, pp 360–369

    Google Scholar 

  31. Herrera F, Lozano M, Verdegay JL (1995) Tuning fuzzy logic controllers by genetic algorithms. Int J Approximate Reasoning 12:299–315

    Article  MATH  MathSciNet  Google Scholar 

  32. Herrera F, Lozano M, Verdegay JL (1995) Tackling fuzzy genetic algorithms. In: Winter G, Periaux J, Galan M, Cuesta P (eds.) Genetic algorithms in engineering and computer science, Wiley, USA, pp 167–189

    Google Scholar 

  33. Hoffmann F (1999) The Role of Fuzzy Logic in Evolutionary Robotics. In: Saffiotti A, Driankov D (ed.) Fuzzy logic techniques for autonomous vehicle navigation, Springer, Berlin Heidelberg New York

    Google Scholar 

  34. Holland JH (1975) Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  35. Holland JH, Reitman JS (1978), Cognitive systems based on adaptive algorithms. In: Waterman DA, Hayes-Roth F (eds.) Pattern-directed inference systems. Academic, San Diego, CA

    Google Scholar 

  36. Holland, JH (1980) Adaptive algorithms for discovering and using general patterns in growing knowledge bases. Int J Policy Anal Inf Sys 4(3):245–268

    Google Scholar 

  37. Hui LY (2007) Evolutionary neural network modeling for forecasting the field failure data of repairable systems. Expert Syst Appl 33(4):1090–1096

    Article  MathSciNet  Google Scholar 

  38. Ishibuchi H, Nojima Y (2007) Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int J Approximate Reason 44(1):4–31

    Article  MATH  MathSciNet  Google Scholar 

  39. Juang CF, Chung IF (2007) Recurrent fuzzy network design using hybrid evolutionary learning algorithms, Neurocomputing 70(16–18):3001–3010

    Article  Google Scholar 

  40. Jin Y, von Seelen W (1999) Evaluating flexible fuzzy controllers via evolution strategies. Fuzzy Sets Syst 108(3):243–252

    Article  Google Scholar 

  41. Kennedy J, Eberhart RC (1995). Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp 1942–1948

    Google Scholar 

  42. Kim KJ, Cho SB (2006) Evolved neural networks based on cellular automata for sensory-motor controller. Neurocomputing 69(16–18):2193–2207

    Article  Google Scholar 

  43. Kim KJ (2006) Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Syst Appl 30(3):519–526

    Article  Google Scholar 

  44. Kitano H (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Syst 4(4):461–476

    MATH  Google Scholar 

  45. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection, MIT, Cambridge, MA

    MATH  Google Scholar 

  46. Kosinski W (2007) Evolutionary algorithm determining defuzzyfication operators. Eng Appl Artif Intell 20(5):619–627

    Article  Google Scholar 

  47. Kelesoglu O (2007) Fuzzy multiobjective optimization of truss-structures using genetic algorithm. Adv Eng Softw 38(10):717–721

    Article  Google Scholar 

  48. Lin CM (2006) Multiobjective fuzzy competence set expansion problem by multistage decision-based hybrid genetic algorithms. Appl Math Comput 181(2):1402–1416

    Article  MATH  Google Scholar 

  49. Liu Y, Yao X (1996) Evolutionary design of artificial neural networks with different node transfer functions. In: Proceedings of the Third IEEE International Conference on Evolutionary Computation, Nagoya, Japan, pp 670–675

    Google Scholar 

  50. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13

    Article  MATH  Google Scholar 

  51. Marwala T (2007) Bayesian training of neural networks using genetic programming. Pattern Recognit Lett 28(12):1452–1458

    Article  Google Scholar 

  52. Mascioli F, Martinelli G (1995) A constructive algorithm for binary neural networks: the oil spot algorithm. IEEE Trans Neural Netw 6(3):794–797

    Article  Google Scholar 

  53. Merril JWL, Port RF (1991) Fractally configured neural networks. Neural Netw 4(1):53–60

    Article  Google Scholar 

  54. Moriarty DE, Miikkulainen R (1997) Forming neural networks through efficient and adaptive coevolution. Evol Comput 5:373–399

    Article  Google Scholar 

  55. Mohammadian M, Stonier RJ (1994) Generating fuzzy rules by genetic algorithms. In: Proceedings of 3rd IEEE International Workshop on Robot and Human Communication, Nagoya, pp 362–367

    Google Scholar 

  56. Muhlenbein H, Paab G (1996) From recombination of genes to the estimation of distributions I. Binary parameters. In: Lecture notes in computer science 1411: parallel problem solving from nature-PPSN IV, pp 178–187

    Google Scholar 

  57. Oh SK, Pedrycz W, Roh SB (2006), Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons. Inf Sci 176(23):3490–3519

    Article  MATH  Google Scholar 

  58. Omlin CW, Giles CL (1993) Pruning recurrent neural networks for improved generalization performance. Techincal report No 93-6, CS Department, Rensselaer Institute, Troy, NY

    Google Scholar 

  59. Cordon O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases, World Scientific, Singapore, ISBN 981-02-4016-3, p 462

    MATH  Google Scholar 

  60. Park HS, Pedrycz W, Oh SK (2007) Evolutionary design of hybrid self-organizing fuzzy polynomial neural networks with the aid of information granulation. Expert Syst Appl 33(4):830–846

    Article  Google Scholar 

  61. Pawar PM, Ganguli R (2007) Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades. Mech Syst Signal Process 21(5):2212–2236

    Article  Google Scholar 

  62. Pedrycz W (ed.) (1997), Fuzzy evolutionary computation, Kluwer Academic Publishers, Boston, ISBN 0-7923-9942-0, p 336

    MATH  Google Scholar 

  63. Pettersson F, Chakraborti N, Saxen H (2007) A genetic algorithms based multi-objective neural net applied to noisy blast furnace data. Appl Soft Comput 7(1):387–397

    Article  Google Scholar 

  64. Rokach L, Maimon O (2005) Clustering methods, data mining and knowledge discovery handbook, Springer, Berlin Heidelberg New York, pp 321–352

    Book  Google Scholar 

  65. Sexton R, Dorsey R, Johnson J (1999) Optimization of neural networks: a comparative analysis of the genetic algorithm and simulated annealing. Eur J Oper Res 114:589–601

    Article  MATH  Google Scholar 

  66. Stepniewski SW, Keane AJ (1997) Pruning back-propagation neural networks using modern stochastic optimization techniques. Neural Comput Appl 5:76–98

    Article  Google Scholar 

  67. Storn R, Price K (1997) Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. J Global Optim 11(4):341–359

    Article  MATH  MathSciNet  Google Scholar 

  68. Rechenberg I, (1973) Evolutions strategie: optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Fromman-Holzboog, Stuttgart

    Google Scholar 

  69. Schwefel HP (1977) Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie, Birkhaeuser, Basel

    Google Scholar 

  70. Serra GLO, Bottura CP (2006) Multiobjective evolution based fuzzy PI controller design for nonlinear systems. Eng Appl Artif Intell 19(2):157–167

    Article  Google Scholar 

  71. Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD thesis, University of Pittsburgh

    Google Scholar 

  72. Takagi T, Sugeno M (1983) Derivation of fuzzy logic control rules from human operators control actions. In: Proceedings of the IFAC symposium on fuzzy information representation and decision analysis, pp 55–60

    Google Scholar 

  73. Tsang CH, Kwong S, Wang A (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit 40(9):2373–2391

    Article  MATH  Google Scholar 

  74. Wang H, Kwong S, Jin Y, Wei W, Man K (2005) A multi-objective hierarchical genetic algorithm for interpretable rule-based knowledge extraction. Fuzzy Sets Syst 149(1):149–186

    Article  MATH  MathSciNet  Google Scholar 

  75. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  76. Xu R, Wunsch D, (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678

    Article  Google Scholar 

  77. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):423–1447

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center of Excellence for Quantifiable Quality of Service, Norwegian University of Science and Technology, Trondheim, Norway

    Ajith Abraham

  2. Department of Computer Science Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Romania

    Crina Grosan

Authors
  1. Ajith Abraham
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  2. Crina Grosan
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Editors and Affiliations

  1. Centre for Quantifiable Quality of Service in Communication Systems (Q2S), Centre of Excellence Norwegian University of Science and Technology, O.S. Bragstads plass 2E, N-7491, Trondheim, Norway

    Ajith Abraham

  2. Department of Computer Science Faculty of Mathematics and Computer Science, Babes-Bolyai University, Cluj-Napoca, Kogalniceanu 1, 400084, Cluj - Napoca, Romania

    Crina Grosan

  3. Department of Electrical & Computer Engineering, University of Alberta, ECERF Bldg., 2nd floor, Edmonton, AB, T6G 2V4, Canada

    Witold Pedrycz

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Abraham, A., Grosan, C. (2008). Engineering Evolutionary Intelligent Systems: Methodologies, Architectures and Reviews. In: Abraham, A., Grosan, C., Pedrycz, W. (eds) Engineering Evolutionary Intelligent Systems. Studies in Computational Intelligence, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75396-4_1

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  • DOI: https://doi.org/10.1007/978-3-540-75396-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75395-7

  • Online ISBN: 978-3-540-75396-4

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