The automated design of artificial neural networks using evolutionary computation

Studies in Computational Intelligence

Volumn 92, Issue , 2008, Pages 19-41

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

^a University of Maryland ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 41449103770     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-76286-7_2     Document Type: Article

References (52)

1. Abraham A (2002) Optimization of evolutionary neural networks using hybrid learning algorithms. In: Proceedings of the 2002 International Joint Conference on Neural Networks (IJCNN '02), IEEE, vol. 3, pp. 2797-2802
2. Balakrishnan K, Honavar V (2001) Evolving neuro-controllers and sensors for artificial agents. In: Advances in the Evolutionary Synthesis of Intelligent Agents, MIT, pp. 109-152
3. Banzhf W, Nordin P, Keller RE, Francone FD (1997) Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco, CA
4. Belew RK, McInerney J, Schraudolph NN (1991) Evolving networks: Using the genetic algorithm with connectionist learning. CSE Technical Report #CS90-174, Computer Science and Engineering Department, UCSD
5. Bentely PJ, Corne DW (2001) Creative Evolutionary Systems. Morgan Kaufmann, San Francisco, CA
6. Blum AL, Rivest RL (1992) Training a 3-node neural network is np-complete. Neural Networks 5(1):117-127
7. Bonissone PP, Subbu R, Eklund N, Kiehhl TR (2006) Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Transactions on Evolutionary Computation 10(3):256-280
8. Brown M, Keynes R, Lumsden A (2001) The Developing Brain. Oxford University Press, Oxford
9. Cangelosi A, Parisi D, Nolfi S (1994) Cell division and migration in a genotype for neural networks. Network: Computation in Neural Systems 5(4):497-515
10. Chalmers DJ (1990) The evolution of learning: An experiment in genetic connectionism. In: Touretsky DS, Elman JL, Sejnowski TJ, Hinton GE (eds.) Proceedings of the 1990 Connectionist Summer School, Morgan Kaufmann, pp. 81-90, URL citeseer.ist.psu.edu/chalmers90evolution.html
11. Chong SY, Tan MK, White JD (2005) Observing the evolution of neural networks learning to play the game of othello. IEEE Transactions on Evolutionary Computation 9(3):240-251
12. Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 2(4):303-314
13. De Garis H (1991) GenNETS: Genetically programmed neural nets using the genetic algorithm to train neural nets whose inputs and/or output vary in time. In: Proceedings of the International Joint Conference on Neural Networks (5th IJCNN '91), IEEE, Singapore, vol. 2, pp. 1391-1396
14. Dill FA, Deer BC (1991) An exploration of genetic algorithms for the selection of connection weights in dynamical neural networks. In: Proceedings of the IEEE 1991 National Aerospace and Electronics Conference (NAECON '91), IEEE, New York, NY, Dayton, OH, vol. 3, pp. 1111-1115
15. Dimond S, Blizard D (1977) Evolution and Lateralization of the Brain. New York Academy of Sciences, New York
16. Elman JE (1990) Finding structure in time. Cognitive Science 14(2):179-211
17. Ferdinando AD, Calabretta R, Parisi D (2001) Evolving modular architectures for neural networks. In: French R, Sougné J (eds.) Proceedings Sixth Neural Computation and Psychology Workshop Evolution, Learning, and Development
18. Giles CL, Miller CB, Chen D, Sun GZ, Chen HH, Lee YC (1992) Extracting and learning an unknown, grammar with recurrent neural networks. In: Moody JE, Hanson SJ, Lippmann RP (eds.) Advances in Neural Information Processing Systems 4, Morgan Kaufmann, Denver, CO, pp. 317-324
19. Gruau F (1995) Automatic definition of modular neural networks. Adaptive Behavior 3:151-183
20. Gruau F, Whitley D, Pyeatt L (1996) A comparison between cellular encoding and direct encoding for genetic neural networks. In: Proceedings of the Sixth International Conference on Genetic Programming, Stanford University Press
21. Grushin A, Reggia JA (2005) Evolving processing speed asymmetries and hemispheric interactions in a neural network model. Neurocomputing 65:47-53
22. Harp S, Samad T, Guha A (1989) Towards the genetic synthesis of neural networks. In: Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 360-369
23. Haykin S (1999) Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River, NJ
24. Jordan MI (1986) Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the Eighth Conference of the Cognitive Science Society, Erlbaum, pp. 531-546
25. Jung JY, Reggia JA (2004) A descriptive encoding language for evolving modular neural networks. In: Genetic and Evolutionary Computation - GECCO-2004, Part II, Springer, Lecture Notes in Computer Science, vol. 3103, pp. 519-530
26. Jung JY, Reggia JA (2006) Evolutionary design of neural network architectures using a descriptive encoding language. IEEE Transactions on Evolutionary Computation 10:676-688
27. Kandel E, Schwartz J, Jessel T (1991) Principles of Neural Science. Appleton and Lange, Norwalk, CT
28. Killackey H (1996) Evolution of the human brain: A neuroanatomical perspective. In: Gazzaniga M (ed.) The Cognitive Neurosciences, MIT, pp. 1243-1253
29. Kitano H (1994) Neurogenetic learning: An integrated method of designing and training neural networks using genetic algorithms. Physica D 75:225-238
30. Koza J, Bennett F, Andre D, Keane M (1999) Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann, San Francisco, CA
31. Lehmann KA, Kaufmann M (2005) Evolutionary algorithms for the self-organized evolution of networks. In: GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM, New York, NY, USA, pp. 563-570, DOI http://doi.acm.org/10.1145/1068009.1068105
32. Mehrotra K, Mohan CK, Ranka S (1997) Elements of Artificial Neural Networks. MIT, Cambridge, MA
33. Miller GF, Todd PM, Hegde SU (1989) Designing neural networks using genetic algorithms. In: Proceedings of third International Conference on Genetic algorithms (ICGA89), pp. 379-384
34. Mitchell M (1996) An Introduction to Genetic Algorithms. MIT, Cambridge, MA
35. Montana D, Davis L (1990) Training feedforward neural networks using genetic algorithms. In: Proceedings of eleventh International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 370-374
36. Mountcastle V (1998) The Cerebral Cortex. Harvard University Press, Cambridge, MA
37. Pérez-Ortiz J, Calera-Rubio J, Forcada M (2001) A comparison between recurrent neural architectures for real-time nonlinear prediction of speech signals. In: Miller D, Adali T, Larsen J, Hulle MV, Douglas S (eds.) Neural Networks for Signal Processing XI, Proceedings of the 2001 IEEE Neural Networks for Signal Processing Workshop (NNSP '01), IEEE Signal Processing Society, pp. 73-81
38. Radi A, Poli R (1998) Genetic programming can discover fast and general learning rules for neural networks. In: Koza JR, Banzhaf W, Chellapilla K, Deb K, Dorigo M, Fogel DB, Garzon MH, Goldberg DE, Iba H, Riolo R (eds.) Genetic Programming 1998: Proceedings of the Third Annual Conference, Morgan Kaufmann, University of Wisconsin, Madison, Wisconsin, USA, pp. 314-323
39. Radio MJ, Reggia JA, Berndt RS (2001) Learning word pronunciations using a recurrent neural network. In: Proceedings of International Joint Conference on Neural Networks (IJCNN '01), vol. 1, pp. 11-15
40. Riedmiller M (1994) Advanced supervised learning in multi-layer perceptrons: From backpropagation to adaptative learning algorithms. Computer Standards & Interfaces 16(3):265-278
41. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: Proceedings of 1993 IEEE International Conference on Neural Networks, vol. 1, pp. 586-591
42. Ruppin E (2002) Evolutionary autonomous agents: A neuroscience perspective. Nature Reviews Neuroscience 3(2):132-141
43. Saravanan N, Fogel D (1995) Evolving neural control systems. IEEE Expert 10:23-27
44. Sejnowski T, Rosenberg C (1987) Parallel networks that learn to pronounce english text. Complex Systems 1:145-168
45. Shkuro Y, Reggia JA (2003) Cost minimization during simulated evolution of paired neural networks leads to asymmetries and specialization. Cognitive Systems Research 4(4):365-383
46. Srinivas M, Patnaik LM (1991) Learning neural network weights using genetic algorithms- improving performance by search-space reduction. In: 1991 IEEE International Joint Conference on Neural Networks, IEEE, Singapore, vol. 3, pp. 2331-2336
47. Stanley KO, Miikkulainen R (2002) Evolving neural network through augmenting topologies. Evolutionary Computation 10(2):99-127
48. Tooby J, Cosmides L (2000) Toward mapping the evolved functional organization of mind and brain. In: Gazzinga M (ed.) The New Cognitive Neurosciences, MIT, pp. 1167-1178
49. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1):67-82
50. Wolpert DH, Macready WG (2005) Coevolutionary free lunches. IEEE Transactions on Evolutionary Computation 9(6):721-735
51. Yao X (1999) Evolving artificial neural networks. Proceedings of the IEEE 87(9):1423-1447
52. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Transactions on Evolutionary Computation 3(4):257-271