Evolution of neural networks using Cartesian Genetic Programming

2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Volumn , Issue , 2010, Pages

  Khan, Maryam Mahsal ^a   Khan, Gul Muhammad ^a   Miller, Julian F ^b  

^a UNIVERSITY OF ENGINEERING AND TECHNOLOGY   (Pakistan)

^b UNIVERSITY OF YORK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CARTESIAN GENETIC PROGRAMMING; NEUROEVOLUTION; POLE BALANCING;

BIOLOGY; GENETIC ALGORITHMS; NEURAL NETWORKS;

GENETIC PROGRAMMING;

EID: 79959463565     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2010.5586547     Document Type: Conference Paper

References (26)

1. J. C. Bongard and R. Pfeifer. Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny. In Spector et al. 2001, pages 829-836, 2001.
2. C. Conforth and Y. Meng. Toward evolving neural networks using bioinspired algorithms. In Proceedings of the International Conference on Artificial Intelligence, pages 413-419, 2008.
3. D. Dasgupta and D. McGregor. Designing application-specific neural networks using the structured genetic algorithm. In Proceedings of the International Conference on Combinations of Genetic Algorithms and Neural Networks, pages 87-96, 1992.
4. N. García-Pedrajas, C. Hervás-Martínez, and J. Mu noz-Pérez. Covnet: A cooperative coevolutionary model for evolving artificial neural networks. IEEE Transactions on Neural Networks, 14(3), 2003.
5. F. Gomez, J. Schmidhuber, and R. Miikkulainen. Accelerated neural evolution through cooperatively coevolved synapses. J. Mach. Learn. Res., 9:937-965, 2008.
6. F. J. Gomez and R. Miikkulainen. Solving non-markovian control tasks with neuroevolution. In IJCAI'99: Proceedings of the 16th international joint conference on Artificial intelligence, pages 1356-1361, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.
7. F. J. Gomez, J. Schmidhuber, and R. Miikkulainen. Efficient non-linear control through neuroevolution. In ECML:17th European Conference on Machine Learning, Berlin, Germany, pages 654-662, 2006.
8. F. Gruau. Automatic definition of modular neural networks. Adaptive Behaviour, 3:151-183, 1994.
9. F. Gruau, D. Whitley, and L. Pyeatt. A comparison between cellular encoding and direct encoding for genetic neural networks: In koza, j. r., goldberg, d. e., fogel, d. b., and riolo, r. l., editors, genetic programming 1996. In Proceedings of the First Annual Conference, pages 81-89. MIT Press., 1996.
10. G. S. Hornby and J. B. Pollack. Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8, 2002.
11. J. Koutnik, F. Gomez, and J. Schmidhuber. Searching for minimal neural networks in fourier space. In Proceedings of the Third Conference on Artificial General Intelligence, page to appear, 2010.
12. C.-H. Lee and J.-H. Kim. Evolutionary ordered neural network with a linked-list encoding scheme. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pages 665-669, 1996.
13. M. Mandischer. Representation and evolution of neural networks. in albrecht, r. f., reeves, c. r., and steele, u. c., editors. Artificial Neural Nets and Genetic Algorithms, pages 643-649, 1993.
14. V. Maniezzo. Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks, 5(1):39-53, 1994.
15. C. G. Miguel, C. F. d. Silva, and M. L. Netto. Structural and parametric evolution of continuous-time recurrent neural networks. In SBRN '08: Proceedings of the 2008 10th Brazilian Symposium on Neural Networks, pages 177-182. IEEE Computer Society, 2008.
16. J. F. Miller and P. Thomson. Cartesian genetic programming. In Proc. of the 3rd European Conf. on Genetic Programming, volume 1802, pages 121-132, 2000.
17. D. E. Moriarty, R. Miikkulainen, B. Kuipers, R. Mooney, B. Porter, and J. Shavlik. Symbiotic evolution of neural networks in sequential decision tasks. In Evolutionary Synthesis of Neural Systems. MIT-Press, 1997.
18. K. Ohkura, T. Yasuda, Y. Kawamatsu, Y. Matsumura, and K. Ueda. Mbeann: Mutation-based evolving artificial neural networks. In ECAL, pages 936-945, 2007.
19. D. W. Opitz and J. W. Shavlik. Connectionist theory refinement: Genetically searching the space of network topologies. Journal of Artificial Intelligence Research, 6:177-209, 1997. (Pubitemid 127667589)
20. K. Stanley and R. Miikkulainen. Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research, 3:21:63-100, 2004. (Pubitemid 41525898)
21. K. O. Stanley and R. Miikkulainen. Efficient evolution of neural network topologies. In CEC '02: Proceedings of the Evolutionary Computation on 2002., pages 1757-1762, Washington, DC, USA, 2002. IEEE Computer Society.
22. Y. Tsoy and V. Spitsyn. Using genetic algorithm with adaptive mutation mechanism for neural networks design and training. In proceeding of third international conference on Genetic Algorithms, page 709 714, 2005.
23. Wieland. Evolving neural network controllers for unstable systems. In Proceedings of the International Joint Conference on Neural Networks (Seattle, WA), pages 667-673. NJ: IEEE., 1991.
24. X. Yao. Evolving artificial neural networks. In Proceedings of the IEEE, volume 87(9), pages 1423-1447, 1999.
25. X. Yao and Y. Liu. Towards designing artificial neural networks by evolution. Applied Mathematics and Computation, 91(1):83-90, 1996.
26. B. Zhang and H. Muhlenbein. Evolving optimal neural networks using genetic algorithms with occams razor. Complex Systems, 7:199-220, 1993.