Learning fuzzy cognitive maps from data by ant colony optimization

GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation

Volumn , Issue , 2012, Pages 9-16

  Chen, Ye ^a   Mazlack, Lawrence ^b   Lu, Long ^c  

^a 705 Engineering Research Center   (United States)

^b UNIVERSITY OF CINCINNATI   (United States)

^c CINCINNATI CHILDREN'S HOSPITAL MEDICAL CENTER   (United States)

Author keywords

ant colony optimization; data driven learning algorithm; fuzzy cognitive maps; numerical optimization

Indexed keywords

ACO ALGORITHMS; ANT COLONY OPTIMIZATION (ACO); CAUSAL RELATIONSHIPS; EXPERT KNOWLEDGE; FUZZY COGNITIVE MAP; HEBBIAN LEARNING; LEARNING PROBLEM; MODEL ERRORS; MODELING TECHNIQUE; MULTIPLE RESPONSE; NUMERICAL OPTIMIZATIONS; SIMULATED DATA;

EXPERIMENTS; FUZZY RULES; FUZZY SYSTEMS; LEARNING ALGORITHMS;

ARTIFICIAL INTELLIGENCE;

EID: 84864714121     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2330163.2330166     Document Type: Conference Paper

References (22)

1. Kosko, B. Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24, 1 (1986), 65-75.
2. Stach, W., Kurgan, L. and Pedrycz, W. Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. In Proceedings of the 2008 IEEE International Conference on Fuzzy Systems, FUZZ 2008, (Hong Kong, China, June 1 - June 6, 2008), 1975-1981.
3. Stach, W., Kurgan, L., Pedrycz, W. and Reformat, M. Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems, 153, 3 (2005), 371-401.
4. Papageorgiou, E. I., Parsopoulos, K. E., Stylios, C. S., Groumpos, P. P. and Vrahatis, M. N. Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization. Journal of Intelligent Information Systems, 25(2005), 95-121.
5. Ghazanfari, M., Alizadeh, S., Fathian, M. and Koulouriotis, D. E. Comparing simulated annealing and genetic algorithm in learning FCM. Applied Mathematics and Computation, 192, 1 (2007), 56-68.
6. Stach, W., Kurgan, L. and Pedrycz, W. A divide and conquer method for learning large fuzzy cognitive maps. Fuzzy Sets and Systems, 161, 19 (2010), 2515-2532.
7. Stach, W. Learning and Aggregation of Fuzzy Cognitive Maps - An Evolutionary Approach. PhD Dissertation, University of Alberta, 2010.
8. Chandra Mohan, B. and Baskaran, R. A survey: Ant Colony Optimization based recent research and implementation on several engineering domain. Expert Systems with Applications, 39, 4 (2012), 4618-4627.
9. Dorigo, M., Maniezzo, V. and Colorni, A. Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26, 1 (1996), 29-41.
10. Liao, T., Oca, M. A. M. d., Aydin, D., Stützle, T. and Dorigo, M. An incremental ant colony algorithm with local search for continuous optimization. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (Dublin, Ireland, 2011), 125-132.
11. Xiao-Min, H., Jun, Z., Chung, H. S. H., Yun, L. and Ou, L. SamACO: Variable Sampling Ant Colony Optimization Algorithm for Continuous Optimization. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 40, 6 (2010), 1555-1566.
12. Ding, Z., Li, D. and Jia, J. First Study of Fuzzy Cognitive Map Learning Using Ants Colony Optimization. Journal of Computational Information Systems, 7, 13 (2011), 4756-4763.
13. Chen, N. and Zhang, J. Index-based genetic algorithm for continuous optimization problems. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (Dublin, Ireland, 2011), 1029-1036.
14. Tsadiras, A. K. Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps. Information Sciences, 178, 20 (2008), 3880-3894.
15. Papageorgiou, E. I., Stylios, C. D. and Groumpos, P. P. Active Hebbian learning algorithm to train fuzzy cognitive maps. International Journal of Approximate Reasoning, 37, 3 (2004), 219-249.
16. Koulouriotis, D. E., Diakoulakis, I. E. and Emiris, D. M. Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling and simulating high-level behavior. In Proceedings of the Congress on Evolutionary Computation (Soul, Korea, Republic of, 2001), 364-371.
17. Stach, W., Kurgan, L., Pedrycz, W. and Reformat, M. Evolutionary development of fuzzy cognitive maps. In Proceedings of the IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2005 (Reno, NV, United states, May 22 - May 25, 2005), 619-624.
18. Parsopoulos, K. E., Papageorgiou, E. I., Groumpos, P. P. and Vrahatis, M. N. A first study of fuzzy cognitive maps learning using particle swarm optimization. In Proceedings of the The 2003 Congress on Evolutionary Computation, 2003. CEC '03 (2003), 1440-1447.
19. Alizadeh, S., Ghazanfari, M., Jafari, M. and Hooshmand, S. Learning FCM by Tabu Search. International Journal of Computer Science, 2, 2 (2007), 142-149.
20. Alizadeh, S. and Ghazanfari, M. Learning FCM by chaotic simulated annealing. Chaos, Solitons & Fractals, 41, 3 (2009), 1182-1190.
21. Christian, B. Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2, 4 (2005), 353-373.
22. Papageorgiou, E. I., Stylios, C. D. and Groumpos, P. P. Fuzzy cognitive map learning based on nonlinear Hebbian rule. In Proceedings of the Australian Conference on Artificial Intelligence (2003), 256-268.