Hybridization of evolutionary algorithms and local search by means of a clustering method

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 36, Issue 3, 2006, Pages 534-545

  Martínez Estudillo, Alfonso C ^a   Hervás Martínez, César ^a   Martínez Estudillo, Francisco J ^a   García Pedrajas, Nicolás ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Clustering; Evolutionary algorithms (EAs); Hybridization; Product units networks

Indexed keywords

NEURAL NETWORKS; NONLINEAR EQUATIONS; OPTIMIZATION; REGRESSION ANALYSIS;

CLUSTERING METHOD; HYBRID ALGORITHMS; HYBRIDIZATION; PRODUCT-UNITS NETWORKS;

EVOLUTIONARY ALGORITHMS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CLUSTER ANALYSIS; EVOLUTION; INFORMATION RETRIEVAL; METHODOLOGY; NONLINEAR SYSTEM; REGRESSION ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; EVOLUTION; INFORMATION STORAGE AND RETRIEVAL; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; REGRESSION ANALYSIS;

EID: 33744512928     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCB.2005.860138     Document Type: Article

References (53)

1. C. R. Houck, J. A. Joines, and M. G. Kay, "Comparison of genetic algorithms, random start, and two-opt switching for solving large location-allocation problems," Comput. Oper. Res., vol. 23, no. 6, pp. 587-596, 1996.
2. C. R. Houck, J. A. Joines, and M. G. Kay, "Empirical investigation of the benefits of partial Lamarckianism," Evol. Comput., vol. 5, no. 1, pp. 31-60, 1997.
3. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. New York: Springer-Verlag, 1994.
4. A. A. Torn and A. Zilinskas, "Global optimization," in Lecture Notes in Computer Science, vol. 350. Berlin, Germany: Springer-Verlag, 1987, pp. 255-263.
5. A. Kollen and E. Pesch, "Genetic local search in combinatorial optimization," Discrete Appl. Math. Comb. Oper. Res. Comput. Sci., vol. 48, no. 3, pp. 273-284, 1994.
6. N. L. J. Ulder, E. H. L. Aarts, H. J. Bandelt, P. J. M. Laarhoven, and E. Pesch, "Genetic local search algorithms for the travelling salesman problem," in Parallel Problem Solving from Nature I, vol. 496, H. P. Schwefel and R. Manner, Eds. Berlin, Germany: Springer-Verlag, 1991, pp. 109-116.
7. J. A. Joines and M. G. Kay, "Utilizing hybrid genetic algorithms," in Evolutionary Optimization, R. Sarker, M. Mahamurdian, and X. Yao, Eds. Norwell, MA: Kluwer, 2002.
8. M. Locatelli and F. Schoen, "Random linkage: A family of acceptance/ rejection algorithms for global optimization," Math. Program., vol. 85, no. 2, pp. 379-396, 1999.
9. R. W. Becker and G. V. Lago, "A global optimization algorithm," in Proc. 8th Allerton Conf. Circuits and Systems Theory, Monticello, IL, 1970, pp. 3-12.
10. A. A. Törn, "A search clustering approach to global optimization," in Towards Global Optimization 2, L. C. W. Dixon and G. P. Szegö, Eds. Amsterdam, The Netherlands: North-Holland, 1978.
11. C. G. E. Boender, A. H. G. R. Kan, G. T. Timmer, and L. Stougie, "A stochastic method for global optimization." Math. Program., vol. 22, no. 2, pp. 125-140, 1982.
12. A. H. G. R. Kan and G. T. Timmer, "Stochastic global optimization methods. Part I: Clustering methods," Math. Program., vol. 39, no. 1, pp. 27-56, Sep. 1987.
13. A. H. G. R. Kan and G. T. Timmer, "Stochastic global optimization methods. Part II: Multi level methods," Math. Program., vol. 39, no. 1, pp. 57-78, Sep. 1987.
14. A. A. Torn and S. Viitanen, "Topographical global optimization using pre sampled points," J. Glob. Optim., vol. 5, no. 3, pp. 267-276, 1994.
15. M. M. Ali and C. Storey, "Topographical multilevel single linkage," J. Glob. Optim., vol. 5, no. 4, pp. 349-358, Dec. 1994.
16. G. E. Hinton and S. J. Nolan, "How learning can guide evolution," Complex Syst., vol. 1, no. 3, pp. 495-502, 1987.
17. D. Whitley, S. Gordon, and K. Mathias, "Lamarckian evolution, the Baldwin effect and function optimization," in Parallel Problem Solving From Nature III, Y. Davidor, H. P. Schwefel, and R. Manner, Eds. Berlin, Germany: Springer-Verlag, 1994, pp. 6-15.
18. K. W. C. Ku, M. W. Mak, and W.-C. Siu, "A study of the Lamarckian evolution of recurrent neural networks," IEEE Trans. Evol. Comput., vol. 4, no. 1, pp. 31-42, Apr. 2000.
19. H.-S. Kim and S.-B. Cho, "An efficient genetic algorithm with less fitness evaluation by clustering," in Proc. IEEE Congr. Evolutionary Computation, Seoul, Korea, 2001, pp. 887-894.
20. P. Moscato, "On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms," California Inst. Technol., Pasadena, Tech. Rep. 826, 1989.
21. P. Moscató and C. Cotta, "A gentle introduction to memetic algorithms," in Handbook of Metaheuristics, F. Glower and G. Kochenberger, Eds. Norwell, MA: Kluwer, 1999, pp. 1-56.
22. H. Bersini and B. Renders, "Hybridizing genetic algorithms with hill-climbing methods for global optimization: Two possible ways," in Proc. IEEE Int. Symp. Evolutionary Computation, Orlando, FL, 1994, pp. 312-317.
23. C. J. Merz, "A principal components approach to combining regression estimates," Mach. Learn., vol. 36, no. 1, pp. 9-32, Jul. 1999.
24. J. He, J. Xu, and X. Yao, "Solving equations by hybrid evolutionary computation techniques," IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 295-304, Sep. 2000.
25. Evolutionary Optimization, R. Sarker, M. Mohammadian, and X. Yao, Eds. Norwell, MA: Kluwer, 2002.
26. K. Levenberg, "A method for solution of certain non-linear problems in least squares," Q. J. Appl. Math. II, vol. 2, no. 2, pp. 164-168, 1944.
27. D. W. Marquardt, "An algorithm for least-squares estimation of non-linear parameters," J. Soc. Ind. Appl. Math., vol. 11, no. 2, pp. 431-441, 1963.
28. C. M. Bishop, Neural Networks for Pattern Recognition. London, U.K.: Oxford Univ. Press, 1995.
29. R. Durbin and D. Rumelhart, "Product units: A computationally powerful and biologically plausible extension to backpropagation networks," Neural Comput., vol. 1, no. 1, pp. 133-142, 1989.
30. E. Salinas and L. F. Abbott, "A model of multiplicative neural responses in parietal cortex," Proc. Nat. Acad. Sci. U.S.A., vol. 93, no. 21, pp. 11956-11961, Oct. 1996.
31. M. Leshno, V. L. Lin, A. Pinkus, and S. Schocken, "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function," Neural Netw., vol. 6, no. 6, pp. 861-867, 1993.
32. D. J. Janson and J. F. Frenzel, "Training product unit neural networks with genetic algorithms," IEEE Expert, vol. 8, no. 5, pp. 26-33, Oct. 1993.
33. A. Ismail and A. P. Engelbrecht, "Training product units in feedforward neural networks using particle swarm optimisation," in Proc. Int. Conf. Artificial Intelligence, Development and Practice Artificial Intelligence Techniques, V. B. Bajic and D. Sha, Eds, Durban, South Africa, 1999, pp. 36-40.
34. L. R. Leerink, C. L. Giles, B. G. Horne, and M. A. Jabri, "Learning with product units," in Advances in Neural Information Processing Systems 7. Cambridge, MA: MIT Press, 1995, pp. 537-544.
35. M. Schmith, "On the complexity of computing and learning with multiplicative neural networks," Neural Comput., vol. 14, no. 2, pp. 241-301, Feb. 2002.
36. K. Saito and R. Nakano, "Law discovery using neural networks," in Proc. 15th Int. Joint Conf. Artificial Intelligence (IJCAI), Nagoya, Japan, 1997, pp. 1078-1083.
37. K. Saito and R. Nakano, "Extracting regression rules from neural networks," Neural Netw., vol. 15, no. 10, pp. 1279-1288, Dec. 2002.
38. E. Oost, S. ten Hagen, and F. Schulze, "Extracting multivariate power functions from complex data sets," in Proc. 14th Dutch-Belgian Artificial Intelligence Conf. (BNAIC), Leuven, Belgium, 2002, pp. 235-242.
39. P. J. Angeline, G. M. Saunders, and J. B. Pollack, "An evolutionary algorithm that constructs recurrent neural networks," IEEE Trans. Neural Netw., vol. 5, no. 1, pp. 54-65, Jan. 1994.
40. K. Fukunaga, Introduction to Statistical Pattern Recognition. San Diego, CA: Academic, 1990.
41. S. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, May 1983.
42. J. Friedman, "Multivariate adaptive regression splines (with discussion)," Ann. Stat., vol. 19, no. 1, pp. 1-41, Mar. 1991.
43. J. Carney and P. Cunningham, "Tuning diversity in bagged ensembles," Int. J. Neural Syst., vol. 10, no. 4, pp. 267-279, Aug. 2000.
44. W. M. Lee, C. P. Lim, K. K. Yuen, and S. M. Lo, "A hybrid neural network model for noisy data regression," IEEE Trans. Syst., Man Cybern. B, Cybern., vol. 34, no. 2, pp. 951-960, Apr. 2004.
45. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1999.
46. V. Roth, "The generalized Lasso," IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 16-28, Jan. 2004.
47. H. Drucker, "Improving regressors using boosting techniques," in Proc. 14th Int. Conf. Machine Learning, Nashville, TN, D. H. Fisher, Jr., Ed. San Mateo, CA: Morgan Kaufmann, 1997, pp. 107-115.
48. P. M. Granitto, P. F. Verdes, and H. A. Ceccatto, "Neural network ensembles: Evaluation of aggregation algorithms," Artif. Intell., vol. 163, no. 2, pp. 139-162, Apr. 2005.
49. J. H. J. Huis in't Veld, "Microbial and biochemical spoilage of foods: An overview," Int. J. Food Microbiol., vol. 33, no. 1, pp. 1-18, Nov. 1996.
50. P. Mäkelä, "Lactic acid bacterial contamination at meat processing plants," Ph.D. dissertation, Dept. Food Environ. Hygiene, College Veterinary Medicine, Helsinki, Finland, 1993.
51. K. Björkroth and H. Korkeala, "Evaluation of lactobacillus sake contamination in vacuum packaged sliced cooked meat products by ribotyping," J. Food Prot., vol. 59, no. 4, pp. 398-401, 1996.
52. R. M. García, C. Hervás, M. R. Rodríguez, and G. Zurera, "Modelling the growth of Leuconostoc mesenteroides by means of an artificial neural network," Int. J. Food Microbiol., 2005. in press.
53. J. Baranyi and T. A. Roberts, "A dynamic approach to predicting bacterial growth in food," Int. J. Food Microbiol., vol. 23, no. 3/4, pp. 277-294, Nov. 1994.