A descriptive encoding language for evolving modular neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3103, Issue , 2004, Pages 519-530

  Jung, Jae Yoon ^a   Reggia, James A ^a  

^a University of Maryland ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL LINGUISTICS; ENCODING (SYMBOLS); EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS;

AUTOMATED DESIGN; GENETIC ENCODING; HIERARCHICAL STRUCTURES; HUMAN-READABLE; IN NETWORKS; MODULAR NEURAL NETWORKS; SEARCH SPACES;

RECURRENT NEURAL NETWORKS;

EID: 35048871017     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24855-2_62     Document Type: Article

References (21)

1. K. Balakrishnan and V. Honavar, Properties of genetic representations of neural architectures, Proc. of the World Congress on Neural Networks, pp. 807-813, 1995.
2. C. Coello, Evolutionary multi-objective optimization: a critical review, Evolutionary Optimization, R. Sarkar et al. (eds.), Kluwer, pp. 117-146, 2002.
3. F. Gruau, Neural network synthesis using cellular encoding and the genetic algorithm, Ph.D. Thesis, Laboratoire de l'Informatique du Parallilisme, Ecole Normale Supirieure de Lyon, France, 1994.
4. F. Gruau, Automatic definition of modular neural networks, Adaptive Behavior, 3:151-183, 1995.
5. F. Gruau, D. Whitley, and L. Pyeatt, A comparison between cellular encoding and direct encoding for genetic neural networks, Genetic Programming 1996: Proc. of the First Annual Conference, MIT Press, pp. 81-89, 1996.
6. S. A. Harp, T. Samad, and A. Guha, Toward the genetic synthesis of neural networks, Proc. of 3rd International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 379-384, 1989.
7. T. S. Hussain and R. A. Browse, Network generating attribute grammar encoding, Proc. of International Joint Conference on Neural Networks (IJCNN '98), 1:431-436, 1998.
8. E. R. Kandel and J. H. Schwartz, Principles of Neural Science, Elsevier, 1983.
9. H. Kitano, Designing neural networks using genetic algorithms with graph generation system, Complex Systems, 4:461-476, 1990.
10. H. Kitano, Neurogenetic learning: an integrated method of designing and training neural networks using genetic algorithms. Physica D, 75:225-238, 1994.
11. J. Kodjabachian and J. A. Meyer, Evolution and development of control architectures in animals, Robotics and Autonomous Systems, 16:161-182, 1995.
12. J. Kodjabachian and J. A. Meyer, Evolution and development of modular control architectures for 1-D locomotion in six-legged animats, Connection Science, 10:211-254, 1998.
13. J. R. Koza, Genetic Programming II, MIT Press, 1994.
14. K. W. C. Ku, M. W. Mak, and W. C. Siu, Adding learning to cellular genetic algorithms for training recurrent neural networks, IEEE Trans. on Neural Networks, 10(2):239-252, 1999.
15. S. Luke and L. Spector, Evolving graphs and networks with edge encoding: preliminary report, Late Breaking Papers at the Genetic Programming 1996 Conference, pp. 117-124, 1996.
16. M. Mandischer, Representation and evolution of neural networks, Artificial Neural Nets and Genetic Algorithms, R. F. Albrecht, C. R. Reeves, and N. C. Steele (eds.), Springer, pp. 643-649, 1993.
17. M. J. Radio, J. A. Reggia, and R. S. Berndt, Learning word pronunciations using a recurrent neural network, Proc. of the International Joint Conference on Neural Networks (IJCNN-01), 1:11-15, 2001.
18. M. Riedmiller and H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, Proc. of the IEEE International Conference on Neural Networks, 1:586-591, 1993.
19. W. Schiffmann, Encoding feedforward networks for topology optimization by simulated evolution, Proc. of 4th International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES 2000), 1:361-364, 2000.
20. K. O. Stanley and R. Miikkulainen, Efficient reinforcement learning through evolving neural network topologies, Proc. of the Genetic and Evolutionary Computation Conference (GECCO-2002), Morgan Kaufmann, pp. 569-577, 2002.
21. X. Yao, Evolving artificial neural networks, Proc. of the IEEE, 87(9):1423-1447, 1999.