A hypercube-based encoding for evolving large-scale neural networks

Artificial Life

Volumn 15, Issue 2, 2009, Pages 185-212

  Stanley, Kenneth O ^a   D'Ambrosio, David B ^a   Gauci, Jason ^a  

^a University of Central Florida   (United States)

Author keywords

Artificial embryogeny; Compositional pattern producing networks; CPPNs; Hypercube based NeuroEvolution of Augmenting Topologies; HyperNEAT; Indirect encoding

Indexed keywords

BIOINFORMATICS; ENCODING (SYMBOLS); GEOMETRY; HYPERCUBE NETWORKS; NETWORK CODING; TOPOLOGY; VISION;

ARTIFICIAL EMBRYOGENY; COMPOSITIONAL PATTERN-PRODUCING NETWORKS; CPPNS; HYPERNEAT; INDIRECT ENCODING; NEUROEVOLUTION OF AUGMENTING TOPOLOGIES;

NEURAL NETWORKS;

ARTICLE; BIOLOGY; EVOLUTION; FOOD; METABOLISM; NERVE CELL NETWORK; PHYSIOLOGY; VISION;

COMPUTATIONAL BIOLOGY; EVOLUTION; FOOD; NERVE NET; VISUAL PERCEPTION;

EID: 67650188046     PISSN: 10645462     EISSN: 15309185     Source Type: Journal    
DOI: 10.1162/artl.2009.15.2.15202     Document Type: Article

References (62)

1. Altenberg, L. (1994). Evolving better representations through selective genome growth. In Proceedings of the IEEE World Congress on Computational Intelligence (pp. 182-187). Piscataway, NJ: IEEE Press.
2. Angeline, P. J. (1995). Morphogenic evolutionary computations: Introduction, issues and examples. In J. R. McDonnell, R. G. Reynolds, & D. B. Fogel (Eds.), Evolutionary Programming IV: The Fourth Annual Conference on Evolutionary Programming (pp. 387-401). Cambridge, MA: MIT Press.
3. Angeline, P. J., Saunders, G. M., & Pollack, J. B. (1993). An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5, 54-65.
4. Belew, R. K., & Kammeyer, T. E. (1993). Evolving aesthetic sorting networks using developmental grammars. In S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms. San Francisco: Kaufmann.
5. Bentley, P. J., & Kumar, S. (1999). The ways to grow designs: A comparison of embryogenies for an evolutionary design problem. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999) (pp. 35-43). San Francisco: Kaufmann.
6. Bongard, J. C. (2002). Evolving modular genetic regulatory networks. In Proceedings of the 2002 Congress on Evolutionary Computation.
7. Chklovskii, D. B., & Koulakov, A. A. (2004). Maps in the brain: What can we learn from them? Annual Review of Neuroscience, 27, 369-392.
8. Churchland, P. M. (1986). Some reductive strategies in cognitive neurobiology. Mind, 95, 279-309.
9. Dawkins, R. (1986). The blind watchmaker. Essex, UK: Longman.
10. Dellaert, F. (1995). Toward a biologically defensible model of development. Master's thesis, Case Western Reserve University, Cleveland, OH.
11. Dellaert, F., & Beer, R. D. (1996). A developmental model for the evolution of complete autonomous agents. In P. Maes, M. J. Mataric, J.-A. Meyer, J. Pollack, & S. W. Wilson (Eds.), From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior. Cambridge, MA: MIT Press.
12. Deloukas, P., Schuler, G. D., Gyapay, G., Beasley, E. M., Soderlund, C., Rodriguez-Tome, P., Hui, L., Matise, T. C., McKusick, K. B., Beckmann, J. S., Bentolila, S., Bihoreau, M., Birren, B. B., Browne, J., Butler, A., Castle, A. B., Chiannilkulchai, N., Clee, C., Day, P. J., Dehejia, A., Dibling, T., Drouot, N., Duprat, S., Fizames, C., & Bentley, D. R. (1998). A physical map of 30,000 human genes. Science, 282(5389), 744-746.
13. Eggenberger, P. (1997). Evolving morphologies of simulated 3D organisms based on differential gene expression. In P. Husbands & I. Harvey (Eds.), Proceedings of the Fourth European Conference on Artificial Life (pp. 205-213). Cambridge, MA: MIT Press.
14. Federici, D. (2004). Evolving a neurocontroller through a process of embryogeny. In S. Schaal, A. J. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam, & J.-A. Meyer (Eds.), Proceedings of the Eighth International Conference on Simulation and Adaptive Behavior (SAB-2004) (pp. 373-384). Cambridge, MA: MIT Press.
15. Federici, D. (2004). Using embryonic stages to increase the evolvability of development. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004) Workshop Program. Berlin: Springer-Verlag.
16. Fogel, D. B. (2001). Blondie24: Playing at the Edge of AI. San Francisco: Kaufmann.
17. Gilbert, C. D., & Wiesel, T. N. (1992). Receptive field dynamics in adult primary visual cortex. Nature, 356, 150-152.
18. Gomez, F., & Miikkulainen, R. (1999). Solving non-Markovian control tasks with neuroevolution. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (pp. 1356-1361). San Francisco: Kaufmann.
19. Goodhill, G. J., & Carreira-Perpinn, M. A. (2002). Cortical columns. In L. Nadel (Ed.), Encyclopedia of cognitive science, Vol. 1 (pp. 845-851). London: MacMillan.
20. Green, C. (2003-2006). SharpNEAT homepage. http:// sharpneat.sourceforge.net/.
21. Gruau, F. (1994). Neural network synthesis using cellular encoding and the genetic algorithm. Ph.D. thesis, Ecole Normale Superieure de Lyon, France.
22. Gruau, F., Whitley, D., & Pyeatt, L. (1996). A comparison between cellular encoding and direct encoding for genetic neural networks. In J. R. Koza, D. E. Goldberg, D. B. Fogel, & R. L. Riolo (Eds.), Genetic Programming 1996: Proceedings of the First Annual Conference (pp. 81-89). Cambridge, MA: MIT Press.
23. Hart, W. E., Kammeyer, T. E., & Belew, R. K. (1994). The role of development in genetic algorithms (Technical Report CS94-394). University of California, San Diego.
24. Harvey, I. (1993). The artificial evolution of adaptive behavior. Ph.D. thesis, School of Cognitive and Computing Sciences, University of Sussex.
25. Hornby, G. S., & Pollack, J. B. (2001). The advantages of generative grammatical encodings for physical design. In Proceedings of the 2002 Congress on Evolutionary Computation.
26. Hornby, G. S., & Pollack, J. B. (2002). Creating high-level components with a generative representation for body-brain evolution. Artificial Life, 8(3), 223-246.
27. Hubel, D. H., & Wiesel, T. N. (1965). Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. Journal of Neurophysiology, 28, 229-289.
28. Jakobi, N. (1995). Harnessing morphogenesis. In Proceedings of the Second International Workshop on Information Processing in Cells and Tissues (pp. 29-41).
29. James, D., & Tucker, P. (2005). Evolving a neural network active vision system for shape discrimination. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005) Late Breaking Papers. New York: ACM Press.
30. Kandel, E. R., Schwartz, J. H., & Jessell, T. M. (Eds.). (1991). Principles of neural science (3rd ed.). Amsterdam: Elsevier.
31. Kohl, N., Stanley, K., Miikkulainen, R., Samples, M., & Sherony, R. (2006). Evolving a real-world vehicle warning system. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2006) (pp. 1681-1688).
32. Komosinski, M., & Rotaru-Varga, A. (2001). Comparison of different genotype encodings for simulated 3D agents. Artificial Life, 7(4), 395-418.
33. Lindenmayer, A. (1968). Mathematical models for cellular interaction in development: Parts I, II. Journal of Theoretical Biology, 18, 280-299, 300-315.
34. Lindenmayer, A. (1974). Adding continuous components to L-systems. In G. Rozenberg & A. Salomaa (Eds.), L Systems, Lecture Notes in Computer Science 15 (pp. 53-68). Heidelberg: Springer-Verlag.
35. Martin, A. P. (1999). Increasing genomic complexity by gene duplication and the origin of vertebrates. The American Naturalist, 154(2), 111-128.
36. Miller, J. F. (2004). Evolving a self-repairing, self-regulating, French flag organism. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004). Berlin: Springer-Verlag.
37. Mjolsness, E., Sharp, D. H., & Reinitz, J. (1991). A connectionist model of development. Journal of Theoretical Biology, 152, 429-453.
38. Moriarty, D., & Miikkulainen, R. (1993). Evolving complex Othello strategies with marker-based encoding of neural networks (Technical report AI93-206). Department of Computer Sciences, The University of Texas at Austin.
39. Prusinkiewicz, P., & Lindenmayer, A. (1990). The algorithmic beauty of plants. Heidelberg: Springer-Verlag.
40. Raff, R. A. (1996). The shape of life: Genes, development, and the evolution of animal form. Chicago: University of Chicago Press.
41. Reil, T., & Husbands, P. (2002). Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Transactions on Evolutionary Computation, 6(2), 159-168.
42. Reil, T., & Massey, C. (2001). Biologically inspired control of physically simulated bipeds. Theory in Biosciences, 120, 1-13.
43. Reisinger, J., Stanley, K. O., & Miikkulainen, R. (2005). Towards an empirical measure of evolvability. In Genetic and Evolutionary Computation Conference (GECCO2005) Workshop Program (pp. 257-264). Washington, DC: ACM Press.
44. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In Parallel distributed processing (pp. 318-362). Cambridge, MA: MIT Press.
45. Saravanan, N., & Fogel, D. B. (1995). Evolving neural control systems. IEEE Expert, 10(3), 23-27.
46. Sims, K. (1994). Evolving 3D morphology and behavior by competition. In R. A. Brooks & P. Maes (Eds.), Proceedings of the Fourth International Workshop on the Synthesis and Simulation of Living Systems (Artificial Life IV) (pp. 28-39). Cambridge, MA: MIT Press.
47. Sporns, O. (2002). Network analysis, complexity, and brain function. Complexity, 8(1), 56-60.
48. Stanley, K. O. (2006). Exploiting regularity without development. In Proceedings of the AAAI Fall Symposium on Developmental Systems. Menlo Park, CA: AAAI Press.
49. Stanley, K. O. (2007). Compositional pattern producing networks: A novel abstraction of development. Genetic Programming and Evolvable Machines, 8(2), 131-162.
50. Stanley, K. O., Bryant, B. D., & Miikkulainen, R. (2005). Real-time neuroevolution in the NERO video game. IEEE Transactions on Evolutionary Computation, 9(6), 653-668.
51. Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10, 99-127.
52. Stanley, K. O., & Miikkulainen, R. (2003). A taxonomy for artificial embryogeny. Artificial Life, 9(2), 93-130.
53. Stanley, K. O., & Miikkulainen, R. (2004). Competitive coevolution through evolutionary complexification. Journal of Artificial Intelligence Research, 21, 63-100.
54. Stanley, K. O., & Miikkulainen, R. (2004). Evolving a roving eye for Go. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004). Berlin: Springer-Verlag.
55. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
56. Terzopoulos, D., Rabie, T., & Grzeszczuk, R. (1994). Artificial fishes: Autonomous locomotion, perception, behavior, and learning in a simulated physical world. Artificial Life, 1(4), 327-351.
57. Turing, A. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society B, 237, 37-72.
58. Watson, J. D., Hopkins, N. H., Roberts, J. W., Steitz, J. A., & Weiner, A. M. (1987). Molecular biology of the gene (4th ed.). Menlo Park, CA: Benjamin Cummings.
59. Wolpert, D. H., & Macready, W. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1, 67-82.
60. Yao, X. (1999). Evolving artificial neural networks. Proceedings of the IEEE, 87(9), 1423-1447.
61. Zhao, D., & Jin, W. (2005). The study of cooperative behavior in predator-prey problem of multi-agent systems. In Proceedings of the International Symposium on Autonomous Decentralized Systems (ISADS 2005) (pp. 90-96). Piscataway, NJ: IEEE Press.
62. Zigmond, M. J., Bloom, F. E., Landis, S. C., Roberts, J. L., & Squire, L. R. (Eds.) (1999). Fundamental neuroscience. London: Academic Press.