Evolving artificial neural network structure using grammar encoding and colonial competitive algorithm

Neural Computing and Applications

Volumn 22, Issue SUPPL.1, 2013, Pages 1-16

  Tayefeh Mahmoudi, Maryam ^a,b   Taghiyareh, Fattaneh ^a   Forouzideh, Nafiseh ^a   Lucas, Caro ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

^b IRAN TELECOMMUNICATION RESEARCH CENTER   (Iran)

Author keywords

Artificial neural network (ANN); Artificial neural network structure optimization; Colonial competitive algorithm (CCA); Evolution; Grammar encoding

Indexed keywords

COLONIAL COMPETITIVE ALGORITHMS; CONVENTIONAL ALGORITHMS; EVOLUTION; EVOLVING ARTIFICIAL NEURAL NETWORKS; NETWORK PARAMETERS; NETWORK STRUCTURES; NEURAL NETWORK STRUCTURE OPTIMIZATION; REGRESSION PROBLEM;

ALGORITHMS; ENCODING (SYMBOLS); NETWORK ARCHITECTURE; PARAMETER ESTIMATION;

NEURAL NETWORKS;

EID: 84878020490     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-0905-6     Document Type: Article

References (47)

1. Abdi B, Mozafari H, Ayob A, Kohandel R (2011) Imperialist competitive algorithm and its applications in optimization of laminated composite structures. Eur J Sci Res 55(2): 174-187.
2. Abdechiri M, Faez K, Bahrami H (2010) Neural network learning based on chaotic imperialist competitive algorithm. In: The 2nd international workshop on intelligent system and applications, pp 1-5.
3. Abraham A (2004) Meta learning evolutionary artificial neural networks. Neurocomput J 56: 1-38.
4. Ahmadi MA (2011) Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. J Petrol Explor Prod Technol 1(2-4): 99-106.
5. Alba E, Aldana JF, Troya JM (1993) Fully automatic ANN design: a genetic approach. In: International workshop artificial neural networks (IWANN'93), Lecture Notes in Computer Science, vol 686. Springer, Germany, pp 399-404.
6. Atashpaz E, Hashemzadeh F, Lucas C (2008) Designing MIMO PIID controller using colonial competitive algorithm: applied to distillation column process. In: IEEE world congress on computational intelligence, Hong Kong, pp 1929-1934.
7. Atashpaz E, Hashemzadeh F, Rajabioun R, Lucas C (2008) Colonial competitive algorithm: a novel approach for PID controller design in MIMO distillation column process. Int J Intell Comput Cybern 1(3): 337-355.
8. Atashpaz E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: IEEE congress on evolutionary computation, Singapore, pp 4661-4667.
9. Basiri J, Taghiyareh F, Gazani S (2010) CORER: a new rule generator classifier. In: 13th IEEE international conference on computational science and engineering, Hong Kong, pp 64-71.
10. Broyden CG (1970) The convergence of a class of double-rank minimization algorithms, part I and II. IMA J Appl Math 6(1): 76-90 and 222-231.
11. Castellani M, Rowlands H (2009) Evolutionary artificial neural network design and training for wood veneer classification. J Eng Appl Artif Intell 22(4-5): 732-741.
12. Chen CS, Su SL, Shou HN, Liu WH (2010) Resilient back-propagation neural network for approximation weighted geometric dilution of precision. In: 3rd IEEE international conference on computer science and information technology, pp 53-58.
13. Dai C, Chen W, Zhu Y, Jiang Z, You Z (2011) Seeker optimization algorithm for tuning the structure and parameters of neural networks. Neurocomputing 74(6): 876-883.
14. Dasgupta D, McGregor DR (1992) Designing application-specific neural networks using the structured genetic algorithm. In: IEEE international workshop on combinations of genetic algorithms and neural networks (COGANN'92), Baltimore, pp 87-96.
15. Dasgupta D, McGregor DR (1993) SGA: a structured genetic algorithm. Technical Report, No. IKBS-11-93. ps. Z. Department of Computer Science, University of Strathclyde, Glasgow.
16. Fletcher RE (1970) A new approach to variable metric algorithms. Comput J 13: 317-322.
17. Gruau F (1995) Automatic definition of modular neural networks. Adapt Behav J 3: 151-183.
18. Harp SA, Samad T, Guha A (1989) Toward the genetic synthesis of neural networks. In: 3rd international conference on genetic algorithms and their applications, pp 360-369.
19. He X, Zeng J, Jie J (1998) Artificial neural network weights optimization design based on MEC algorithm. In: International conference on machine learning and cybernetics, vol 6, 3361-3364.
20. Holland JH (1992) Genetic algorithms. Sci Am J 267: 66-72.
21. Hussain TS, Browse RA (1998) Genetic encoding of neural networks using attribute grammars. In: CITO'98. Researcher Retreat, Hamilton.
22. Jacob C, Rehder J (1993) Evolution of neural net architectures by a hierarchical grammar-based genetic system. In: International conference on artificial neural networks and genetic algorithms (ICANNGA'93). Innsbruck. Springer, pp 72-79.
23. Kahramanli H, Allahverdi N (2009) Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst Appl 36(2, Part 1): 1513-1522.
24. Kitano H (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Syst J 4: 461-476.
25. Kitano H (1994) Neurogenetic learning: an integrated method of designing and training neural networks using genetic algorithms. Phys D J 75(1-3): 225-238.
26. Khademolghorani F (2011) An effective algorithm for mining association rules based on imperialist competitive algorithm. In: 6th IEEE international conference on digital information management, Australia, pp 6-11.
27. Khorani V, Forouzideh N, Nasrabadi AM (2011) Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: comparing performances. In: IEEE workshop on hybrid intelligent models and applications, France, pp 61-67.
28. Kyranyaz S, Ice T, Yildirim A, Gabbouj M (2009) Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Netw J 22(10): 1448-1462.
29. Lucas SM (1995) Growing adaptive neural networks with graph grammars. In: European symposium on artificial neural networks (ESANN'95), Brussels, pp 235-240.
30. Mahmoudi MT, Forouzideh N, Lucas C, Taghiyareh F (2009) Artificial neural network weights optimization based on imperialist competitive algorithm. In: 7th International conference on computer science and information technologies (CSIT'09), Yerevan, pp 244-247.
31. Mollaiy S, Shahbazian M (2011) An imperialist competitive algorithm artificial neural network method to predict oil flow rate of the well. Int J Comput Appl 26(10): 47-50.
32. Oskouyi B, Atashpaz E, Soltani N, Lucas C (2009) Application of imperialist competitive algorithm for materials property characterization from sharp indentation test. Int J Eng Simul 10(1).
33. Ozbakir L, Baykasoglu A, Kulluk S, Yapici H (2009) TACO-miner: an ant colony based algorithm for rule extraction from trained neural networks. Expert Syst Appl 36(10): 12295-12305.
34. Ozbakir L, Delice Y (2011) Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer. Eng Appl Artif Intell 24(3): 491-500.
35. Powell MJD (1989) A tolerant algorithm for linearly constrained optimization calculations. Math Program J 45(3): 547-566.
36. Rajabioun R, Atashpaz E, Lucas C (2008) Colonial competitive algorithm as a tool for Nash equilibrium point achievement. In: International conference on computational science and its applications (ICCSA(2)'08), pp 680-695.
37. Rajabioun R, Hashemzadeh F, Atashpaz E, Mesgari B, Salmasi FR (2008) Identification of a MIMO evaporator and its decentralized PID controller tuning using colonial competitive algorithm. In: 17th World congress, the international federation of automatic control, Seoul, pp 9952-9957.
38. Riedmiller M, Braun H (1993) A direct adaptive method for faster back propagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, pp 586-591.
39. Rivero D, Dorado J, Rabuñal JR, Pazos A, Pereira J (2006) Artificial neural network development by means of genetic programming with graph codification. Enformatika Trans Eng Comput Technol 15: 209-214.
40. Rocha M, Cortez P, Neves J (2007) Evolution of neural networks for classification and regression. Neurocomput J 70(16-18): 2809-2816.
41. Sepehrirad H, Lucas C (2007) On the effectiveness of prioritized user-profile and detecting active users in collaborative filtering recommender systems. In: ICDE workshops, pp 863-870.
42. Tsoulos I, Gavrilis D, Glavas E (2008) Neural network construction and training using grammatical evolution. Sci Direct Neurocomput J 72(1-3): 269-277.
43. Tsoulos IG, Lagaris IE (2006) MinFinder: locating all the local minima of a function. Comput Phys Commun J 174(2): 166-179.
44. Turney P, Whitley D, Anderson R (1996) Special issue on the bald-winian effect. Evol Comput J 4(3): 213-329.
45. Vonk E, Jain LC, Johnson R (1995) Using genetic algorithms with grammar encoding to generate neural network. In: IEEE international conference on neural networks, Australia, vol 4, pp 928-1931.
46. Yao X, Liu Y (1998) Toward designing artificial neural networks by evolution. Appl Math Comput J 91(1): 83-90.
47. Yu J, Wang S, Xi L (2008) Evolving artificial neural networks using an improved PSO and DPSO. Neurocomput J 71(4-6): 1054-1060.