Evolving neural networks through augmenting topologies

Evolutionary Computation

Volumn 10, Issue 2, 2002, Pages 99-127

  Stanley, Kenneth O ^a   Miikkulainen, Risto ^a  

^a University of Texas at Austin   (United States)

Author keywords

Competing conventions; Genetic algorithms; Network topologies; Neural networks; Neuroevolution; Speciation

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; HUMAN; LEARNING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LEARNING; MODELS, GENETIC; NEURAL NETWORKS (COMPUTER);

EID: 0036594106     PISSN: 10636560     EISSN: None     Source Type: Journal    
DOI: 10.1162/106365602320169811     Document Type: Article

References (50)

1. Anderson, C. W. (1989). Learning to control an inverted pendulum using neural networks. IEEE Control Systems Magazine, 9:31-37.
2. Angeline, P. J., Saunders, G. M., and Pollack, J. B. (1993). An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5:54-65.
3. Branke, J. (1995). Evolutionary algorithms for neural network design and training. In Alander, J. T., editor, Proceedings First Nordic Workshop on Genetic Algorithms and their Applications, pages 145-163, University of Vaasa Press, Vaasa, Finland.
4. Braun, H. and Weisbrod, J- (1993). Evolving feedforward neural networks. In Albrecht, R. F., Reeves, C. R., and Steele, N. C., editors, Proceedings of ANNGA93, International Conference on Artificial Neural Networks and Genetic Algorithms, pages 25-32, Springer-Verlag, Innsbruck.
5. Chen, D. et al. (1993). Constructive learning of recurrent neural networks. In Petsche, T., Judd, S., and Hanson, S., editors, Computational Learning Theory and Natural Learning Systems III. MIT Press, Cambridge, Massachusetts.
6. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 2(4):303-314.
7. Darnell, J. E. and Doolittle, W. F. (1986). Speculations on the early course of evolution. Proceedings of the National Academy of Sciences, USA, 83:1271-1275.
8. Darwen, P. J. (1996). Co-Evolutionary Learning by Automatic Modularisation with Speciation. Ph.D. thesis, School of Computer Science, University College, University of New South Wales, Sydney, Australia.
9. Darwen, P. and Yao, X. (1996). Automatic modularization by speciation. In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation (ICEC '96), pages 88-93, Nagoya, IEEE Press, Piscataway, New Jersey.
10. Dasgupta, D. and McGregor, D. (1992). Designing application-specific neural networks using the structured genetic algorithm. In Whitley, D. and Schaffer, J. D., editors, Proceedings of the International Conference on Combinations of Genetic Algorithms and Neural Networks, pages 87-96, IEEE Press, Piscataway, New Jersey.
11. Fullmer, B. and Miikkulainen, R. (1992). Using marker-based genetic encoding of neural networks to evolve finite-state behaviour. In Varela, F. J. and Bourgine, P., editors, Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pages 255-262, MIT Press, Cambridge, Massachusetts.
12. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Massachusetts.
13. Goldberg, D. E. and Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization. In Grefenstette, J. J., editor, Proceedings of the Second International Conference on Genetic Algorithms, pages 148-154, Morgan Kaufmann, San Francisco, California.
14. Gomez, F. and Miikkulainen, R. (1997). Incremental evolution of complex general behavior. Adaptive Behavior, 5:317-342.
15. Gomez, F. and Miikkulainen, R. (1999). Solving non-Markovian control tasks with neuroevolution. In Dean, T., editor, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 1356-1361, Morgan Kaufmann, San Francisco, California.
16. Gomez, F. and Miikkulainen, R. (2002). Learning robust nonlinear control with neuroevolution. Technical Report AI02-292, Department of Computer Sciences, The University of Texas at Austin, Austin, Texas.
17. Gruau, F. (1993). Genetic synthesis of modular neural networks. In Forrest, S., editor, Proceedings of the Fifth International Conference on Genetic Algorithms, pages 318-325, Morgan Kaufmann, San Francisco, California.
18. Gruau, F., Whitley, D., and Pyeatt, L. (1996). A comparison between cellular encoding and direct encoding for genetic neural networks. In Koza, J. R. et al., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 81-89, MIT Press, Cambridge, Massachusetts.
19. Harvey, I. (1993). The Artificial Evolution of Adaptive Behavior Ph.D. thesis, School of Cognitive and Computing Sciences, University of Sussex, Sussex, UK.
20. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. University of Michigan Press, Ann Arbor, Michigan.
21. Kaelbling, L. P., Littman, M., and Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence, 4:237-285.
22. Krishnan, R. and Ciesielski, V. B. (1994). Delta-gann: A new approach to training neural networks using genetic algorithms. In Tsoi, A. C. and Downs, T., editors, Proceedings of the Australian Conference on Neural Networks, pages 194-197, University of Queensland, Brisbane, Australia.
23. Lee, C.-H. and Kim, J.-H. (1996). Evolutionary ordered neural network with a linked-list encoding scheme. In Proceedings of the 1996 IEE International Conference on Evolutionary Computation, pages 665-669, IEEE Press, Piscataway, New Jersey.
24. Mahfoud, S. W. (1995). Niching Methods for Genetic Algorithms Ph.D. thesis, Department of General Engineering, IlliGAL Report 95001, University of Illinois at Urbana-Champaign, Urbana, Illinois.
25. Mandischer, M. (1993). 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, Springer Verlag, New York, New York.
26. Maniezzo, V. (1994). Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks, 5(1):39-53.
27. Montana, D. J. and Davis, L. (1989). Training feedforward neural networks using genetic algorithms. In Sridharan, S., editor, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 762-767, Morgan Kaufmann, San Francisco, California.
28. Moriarty, D. E. (1997). Symbiotic Evolution of Neural Networks in Sequential Decision Tasks. Ph.D. thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report UT-AI97-257.
29. Moriarty, D. E. and Miikkulainen, R. (1996). Efficient reinforcement learning through symbiotic evolution. Machine Learning, 22:11-32.
30. Moriarty, D. E. and Miikkulainen, R. (1997). Forming neural networks through efficient and adaptive co-evolution Evolutionary Computation, 5(4):373-399.
31. Opitz, D. W. and Shavlik, J. W. (1997). Connectionist theory refinement: Genetically searching the space of network topologies. Journal of Artificial Intelligence Research, 6:177-209.
32. Pendrith, M. (1994). On reinforcement learning of control actions in noisy and non-Markovian domains. Technical Report UNSW-CSE-TR-9410, School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia.
33. Potter, M. A. and De Jong, K. A. (1995). Evolving neural networks with collaborative species. In Oren, T. I. and Birta, L. G., editors, Proceedings of the 1995 Summer Computer Simulation Conference, pages 340-345, Society for Computer Simulation, San Diego, California..
34. Potter, M. A., De Jong, K. A., and Grefenstette, J. J. (1995). A coevolutionary approach to learning sequential decision rules. In Eshelman, L. J., editor, Proceedings of the Sixth International Conference on Genetic Algorithms, pages 366-372, Morgan Kaufmann, San Francisco, California.
35. Pujol, J. C. F. and Poli, R. (1998). Evolving the topology and the weights of neural networks using a dual representation. Special Issue on Evolutionary Learning of the Applied Intelligence Journal, 8(1):73-84.
36. Radcliffe, N. J. (1993). Genetic set recombination and its application to neural network topology optimisation. Neural Computing and Applications, 1(1):67-90.
37. Radding, C. M. (1982). Homologous pairing and strand exchange in genetic recombination. Annual Review of Genetics, 16:405-437.
38. Rosin, C. D. and Belew, R. K. (1997). New methods for competitive evolution. Evolutionary Computation, 5(1):1-29.
39. Saravanan, N. and Fogel, D. B. (1995). Evolving neural control systems. IEEE Expert, 10(3):23-27.
40. Schaffer, J. D., Whitley, D., and Eshelman, L. J. (1992). Combinations of genetic algorithms and neural networks: A survey of the state of the art. In Whitley, D. and Schaffer, J., editors, Proceedings of the International Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN-92), pages 1-37, IEEE Press, Piscataway, New Jersey.
41. Sigal, N. and Alberts, B. (1972). Genetic recombination: The nature of a crossed strand-exchange between two homologous DNA molecules. Journal of Molecular Biology, 71(3):789-793.
42. Spears, W. (1995). Speciation using tag bits. In Handbook of Evolutionary Computation. IOP Publishing Ltd. and Oxford University Press, Oxford, UK.
43. Thierens, D. (1996). Non-redundant genetic coding of neural networks. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 571-575, IEEE Press, Piscataway, New Jersey.
44. Watson, J. D. et al. (1987). Molecular Biology of the Gene Fourth Edition. The Benjamin Cummings Publishing Company, Inc., Menlo Park, California.
45. Whitley, D. et al. (1993). Genetic reinforcement learning for neurocontrol problems. Machine Learning, 13:259-284.
46. Wieland, A. (1991). Evolving neural network controllers for unstable systems. In Proceedings of the International Joint Conference on Neural Networks, pages 667-673, IEEE Press, Piscataway, New Jersey.
47. Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447.
48. Yao, X. and Liu, Y. (1996). Towards designing artificial neural networks by evolution. Applied Mathematics and Computation, 91(1):83-90.
49. Yao, X. and Shi, Y. (1995). A preliminary study on designing artificial neural networks using coevolution. In Proceedings of the IEEE Singapore International Conference on Intelligent Control and Instrumentation, pages 149-154, IEEE Singapore Section.
50. Zhang, B.-T. and Mühlenbein, H. (1993). Evolving optimal neural networks using genetic algorithms with Occam's razor. Complex Systems, 7:199-220.