Engineering evolutionary intelligent systems: Methodologies, architectures and reviews

Studies in Computational Intelligence

Volumn 82, Issue , 2008, Pages 1-22

  Abraham, Ajith ^a   Grosan, Crina ^b  

^a NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^b BABE BOLYAI UNIVERSITY   (Romania)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 38049000432     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-75396-4_1     Document Type: Review

References (77)

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
2. Abraham, A (2004) Meta-learning evolutionary artificial neural networks. Neurocomput J 56c:1-38
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
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
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
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
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
8. Baxter J (1992) The evolution of learning algorithms for artificial neural networks, Complex systems, IOS, Amsterdam, pp 313-326
9. Bezdek, JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum, New York
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
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
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
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
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
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
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
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
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, Piscataway, NJ, pp 39-43
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
20. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, USA
21. Fontanari JF, Meir R (1991) Evolving a learning algorithm for the binary perceptron, Network, vol. 2, pp 353-359
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
23. Frean M (1990), The upstart algorithm: A method for constructing and training feed forward neural networks. Neural Comput 2:198-209
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
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
26. Grau F (1992)Genetic synthesis of boolean neural networks with a cell rewriting developmental process. In: Whitely D, Scha.er JD (eds.) Proceedings of the international workshop on combinations of genetic algorithms and neural Networks, IEEE Computer Society Press, CA, pp 55-74
27. Gonzalez A, Herrera F (1997) Multi-stage genetic fuzzy systems based on the iterative rule learning approach. Mathware Soft Comput 4(3)
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
29. Gutierrez G, Isasi P, Molina JM, Sanchis A, Galvan IM (2001) Evolutionary cellular con.gurations 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
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
31. Herrera F, Lozano M, Verdegay JL (1995) Tuning fuzzy logic controllers by genetic algorithms. Int J Approximate Reasoning 12:299-315
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
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
34. Holland JH (1975) Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI
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
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
37. Hui LY (2007) Evolutionary neural network modeling for forecasting the field failure data of repairable systems. Expert Syst Appl 33(4):1090-1096
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
39. Juang CF, Chung IF (2007) Recurrent fuzzy network design using hybrid evolutionary learning algorithms, Neurocomputing 70(16-18):3001-3010
40. Jin Y, von Seelen W (1999) Evaluating flexible fuzzy controllers via evolution strategies. Fuzzy Sets Syst 108(3):243-252
41. Kennedy J, Eberhart RC (1995). Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp 1942-1948
42. Kim KJ, Cho SB (2006) Evolved neural networks based on cellular automata for sensory-motor controller. Neurocomputing 69(16-18):2193-2207
43. Kim KJ (2006) Artificial neural networks with evolutionary instance selection for financial forecasting. Expert Syst Appl 30(3):519-526
44. Kitano H (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Syst 4(4):461-476
45. Koza JR (1992) Genetic programming: On the programming of computers by means of natural selection, MIT, Cambridge, MA
46. Kosinski W (2007) Evolutionary algorithm determining defuzzyfication operators. Eng Appl Artif Intell 20(5):619-627
47. Kelesoglu O (2007) Fuzzy multiobjective optimization of truss-structures using genetic algorithm. Adv Eng Softw 38(10):717-721
48. Lin CM (2006) Multiobjective fuzzy competence set expansion problem by multistage decision-based hybrid genetic algorithms. Appl Math Comput 181(2):1402-1416
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
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
51. Marwala T (2007) Bayesian training of neural networks using genetic programming. Pattern Recognit Lett 28(12):1452-1458
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
53. Merril JWL, Port RF (1991) Fractally configured neural networks. Neural Netw 4(1):53-60
54. Moriarty DE, Miikkulainen R (1997) Forming neural networks through efficient and adaptive coevolution. Evol Comput 5:373-399
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
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
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
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
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
60. Park HS, Pedrycz W, Oh SK (2007) Evolutionary design of hybrid selforganizing fuzzy polynomial neural networks with the aid of information granulation. Expert Syst Appl 33(4):830-846
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
62. Pedrycz W (ed.) (1997), Fuzzy evolutionary computation, Kluwer Academic Publishers, Boston, ISBN 0-7923-9942-0, p 336
63. Pettersson F, Chakraborti N, Saxen H (2007) A genetic algorithms based multiobjective neural net applied to noisy blast furnace data. Appl Soft Comput 7(1):387-397
64. Rokach L, Maimon O (2005) Clustering methods, data mining and knowledge discovery handbook, Springer, Berlin Heidelberg New York, pp 321-352
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
66. Stepniewski SW, Keane AJ (1997) Pruning back-propagation neural networks using modern stochastic optimization techniques. Neural Comput Appl 5:76-98
67. Storn R, Price K (1997) Di.erential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. J Global Optim 11(4):341-359
68. Rechenberg I, (1973) Evolutions strategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Fromman-Holzboog, Stuttgart
69. Schwefel HP (1977) Numerische Optimierung von Computermodellen mittels der Evolutionsstrategie, Birkhaeuser, Basel
70. Serra GLO, Bottura CP (2006) Multiobjective evolution based fuzzy PI controller design for nonlinear systems. Eng Appl Artif Intell 19(2):157-167
71. Smith SF (1980) A learning system based on genetic adaptive algorithms. PhD thesis, University of Pittsburgh
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
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
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
75. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67-82
76. Xu R, Wunsch D, (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645-678
77. Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):423-1447