On the issue of separability for problem decomposition in cooperative neuro-evolution

Neurocomputing

Volumn 87, Issue , 2012, Pages 33-40

  Chandra, Rohitash ^a   Frean, Marcus ^a   Zhang, Mengjie ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

Cooperative coevolution; Evolutionary algorithms; Feedforward neural networks; Genetic algorithms and separability; Neuro evolution

Indexed keywords

COOPERATIVE CO-EVOLUTION; LEARNING PROCESS; NEURAL NETWORK TRAINING; NEURO EVOLUTIONS; OPTIMISATIONS; PATTERN CLASSIFICATION PROBLEMS; PROBLEM DECOMPOSITION; PROBLEM-DECOMPOSITION METHODS;

COMPUTER APPLICATIONS; EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS;

FEEDFORWARD NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELL SUBPOPULATION; COOPERATION; COOPERATIVE COEVOLUTION; COOPERATIVE COEVOLUTIONARY NEURAL NETWORK; DECOMPOSITION; EVOLUTION; INFORMATION PROCESSING; LEARNING; MULTIOBJECTIVE COOPERATIVE COEVOLUTION; NEURO EVOLUTION; POSITIVE FEEDBACK; PRIORITY JOURNAL; SYNAPSE;

EID: 84863603865     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.02.005     Document Type: Article

References (23)

1. A. Asuncion, D. Newman, UCI Machine Learning Repository, 2007, URL 〈〉. http://archive.ics.uci.edu/ml/datasets.html.
2. Chandra Rohitash, Frean Marcus, Zhang Mengjie, Omlin Christian W. Encoding subcomponents in cooperative co-evolutionary recurrent neural networks. Neurocomputing, 2011, 74(17):3223-3234.
3. Chandra R., Frean M., Zhang M. An encoding scheme for cooperative coevolutionary feedforward neural networks. AI 2010: Advances in Artificial Intelligence. Lecture Notes in Computer Science 2010, vol. 6464:253-262. Springer, Berlin/Heidelberg.
4. Deb K., Anand A., Joshi D. A computationally efficient evolutionary algorithm for real-parameter optimization. Evol. Comput. 2002, 10(4):371-395.
5. Garcia-Pedrajas N., Hervas-Martinez C., Munoz-Perez J. Multi-objective cooperative coevolution of artificial neural networks (multi-objective cooperative networks). Neural Netw. 2002, 15(10):1259-1278.
6. Garcia-Pedrajas N., Hervas-Martinez C., Munoz-Perez J. COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Trans. Neural Netw. 2003, 14(3):575-596.
7. Garcia-Pedrajas N., Hervas-Martinez C., Ortiz-Boyer D. Cooperative coevolution of artificial neural network ensembles for pattern classification. IEEE Trans. Evol. Comput. 2005, 9(3):271-302.
8. García-Pedrajas N., Ortiz-Boyer D. A cooperative constructive method for neural networks for pattern recognition. Pattern Recogn. 2007, 40(1):80-98.
9. García-Pedrajas N., Ortiz-Boyer D. A cooperative constructive method for neural networks for pattern recognition. Pattern Recogn. 2007, 40(1):80-98.
10. Gomez F., Mikkulainen R. Incremental evolution of complex general behavior. Adapt. Behav. 1997, 5(3-4):317-342.
11. Gomez F., Schmidhuber J., Miikkulainen R. Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res. 2008, 9:937-965.
12. F.J. Gomez, Robust Non-linear Control Through Neuroevolution, Technical Report AI-TR-03-303, PhD Thesis, Department of Computer Science, The University of Texas at Austin, 2003.
13. K. Tang, L. P.N.S.Z.Y. Xiaodong Li, T. Weise, Benchmark Functions for the CEC'2010 Special Session and Competition on Large Scale Global Optimization, Technical Report, Nature Inspired Computation and Applications Laboratory, USTC, China, 2009, URL 〈〉. http://nical.ustc.edu.cn/cec10ss.php.
14. Y. Liu, X. Yao, Q. Zhao, T. Higuchi, Scaling up fast evolutionary programming with cooperative coevolution, in: Proceedings of the 2001 Congress on Evolutionary Computation, 2001.
15. Ortiz-boyer D., Hervs-martnez C., Garca-pedrajas N. Cixl2-a crossover operator for evolutionary algorithms based on population features. J. Artif. Intell. Res. 2005, 24:2005.
16. Potter M.A., DeJong K.A. Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evol. Comput. 2000, 8(1):1-29.
17. M.A. Potter, K.A.D. Jong, A cooperative coevolutionary approach to function optimization, in: PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, Springer-Verlag, London, UK, 1994, pp. 249-257.
18. Salomon R. Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. A survey of some theoretical and practical aspects of genetic algorithms. Biosystems 1996, 39(3):263-278.
19. Schmidhuber J., Wierstra D., Gagliolo M., Gomez F. Training recurrent networks by evolino. Neural Comput. 2007, 19(3):757-779.
20. Shi Y.-j., Teng H.-f., Li Z.-q. Cooperative co-evolutionary differential evolution for function optimization. Advances in Natural Computation. Lecture Notes in Computer Science 2005, vol. 3611:1080-1088. Springer, Berlin/Heidelberg.
21. van den Bergh F., Engelbrecht A. A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 2004, 8(3):225-239.
22. Wong M.L., Lee S.Y., Leung K.S. Data mining of Bayesian networks using cooperative coevolution. Decis. Support Syst. 2004, 38(3):451-472.
23. Yang Z., Tang K., Yao X. Large scale evolutionary optimization using cooperative coevolution. Inf. Sci. 2008, 178(15):2985-2999.