|
Sign In to gain access to subscriptions and/or personal tools.
|
Adaptive Behavior, Vol. 14, No. 4,
315-338 (2006)
DOI: 10.1177/1059712306072335
Increasing Population Diversity Through Cultural Learning
Dara Curran
Department of Information Technology, National University of Ireland, Galway, dara.curran{at}nuigalway.ie
Colm O'Riordan
Department of Information Technology, National University of Ireland, Galway
A number of learning models are commonly employed in the simulation of social behavior. These include population learning, lifetime learning and cultural learning. Population learning allows popula tions as a whole to evolve over time, typically through a Darwinian model of natural selection. Lifetime learning allows individuals to acquire knowledge during their lifetimes and cultural learning allows indi viduals to pass this knowledge to their peers or subsequent generations. This work examines the effects of cultural learning on both the fitness and the diversity of a population of neural network agents. A population employing population learning alone and one employing both population and cultural learning are assigned three benchmark tasks: the 5-bit parity problem, the game of tic-tac-toe and the game of connect-four. Each agent contains a genome which encodes a neural network controller used by the agent to perceive and react to environmental stimuli. Results show that the addition of cultural learning promotes improved fitness and significantly increases both genotypic (the genetic make up of individuals) and phenotypic (the behavior of individuals) diversity in the population.
Key Words: cultural learning diversity learning models artificial life
References
- Baldwin, J. (1896). A new factor in evolution . American Naturalist, 30, 441451 .[CrossRef]
- Belew, R. K. (1990). Evolution, learning and culture: Computational metaphors for adaptive algorithms . Complex Systems, 4, 1149 .
- Belew, R. K., & Booker, L. B. (Eds.). (1991). Proceedings of the 4th International Conference on Genetic Algorithms. San Diego, CA: Morgan Kaufmann.
- Belew, R. K., McInerney, J., & Schraudolph, N. N. (1992). Evolving networks: Using the genetic algorithm with connectionist learning. In C. G. Langton, C.Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial life II (pp. 511547). Redwood City, CA: Addison-Wesley .
- Billard, A., & Hayes, G. (1997). Learning to communicate through imitation in autonomous robots . In 7th International Conference on Artificial Neural Networks (p. 763738 ).
- Blackmore, S. (2000). The power of memes . Scientific American, 284 (4), 5261 .
- Booker, L. B. (1985). Improving the performance of genetic algorithms in classifier systems . In Proceedings of the International Conference on Genetic Algorithms and Their Applications (pp. 8092 ). Pittsburgh, PA.
- Borenstein, E., & Ruppin, E. (2003). Enhancing autonomous agents evolution with learning by imitation . Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour, 1, 335348 .
- Boyd, R., & Richerson, P. (1985). Culture and the evolutionary process. University of Chicago Press .
- Bull, L., Holland, O., & Blackmore, S. (2000). On memegene coevolution . Artificial Life, 6, 227235 .[CrossRef][ISI][Medline]
[Order article via Infotrieve]
- Burke, E. K., Gustafson, S., & Kendall, G. (2004). Diversity in genetic programming: an analysis of measures and correlation with fitness . IEEE Transactions on Evolutionary Computation 8, 4762 .[CrossRef]
- Cangelosi, A. (1999). Evolution of communication using combination of grounded symbols in populations of neural networks . In Proceedings of IJCNN99 International Joint Conference on Neural Networks (Vol. 6, pp. 43654368 ). Washington, DC: IEEE Press.
- Cangelosi, A., & Parisi, D. (1996). The emergence of a language in an Evolving population of neural network (Technical Report NSAL96004). National Research Council, Rome .
- Cangelosi, A., & Parisi, D. (1998). The emergence of a language in an evolving population of neural networks . Connection Science, 10, 8397 .[CrossRef]
- Collins, R. J., & Jefferson, D. R. (1991). Selection in massively parallel genetic algorithms. In R. K. Belew & L. B. Booker (Eds.), ICGA (pp. 249256). San Diego, CA: Morgan Kaufmann .
- Curran, D., & O'Riordan, C. (2003a). Artificial life simulation using marker based encoding . In Proceedings of the 2003 International Conference on Artificial Intelligence (IC-AI 2003) (Vol. II, pp. 665668 ). Las Vegas, NV.
- Curran, D., & O'Riordan, C. (2003b). On the design of an artificial life simulator . In V. Palade, R.J. Howlett, & L. C. Jain (Eds.), Proceedings of the Seventh International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES 2003) (pp. 549555 ). University of Oxford, UK.
- Curran, D., & O'Riordan, C. (2004). The effect of noise on the performance of cultural evolution . In Proceedings of the 2004 Congress on Evolutionary Computation (CEC 2004). Portland, OR.
- Davidor, Y. (1991). A naturally occurring niche and species phenomenon: The model and first results. In R. K. Belew & L. B. Booker (Eds.), ICGA (pp. 257263). San Diego, CA: Morgan Kaufmann .
- Deb, K., & Goldberg, D. E. (1989). An investigation of niche and species formation in genetic function optimization. In Proceedings of the Third International Conference on Genetic Algorithms (pp. 4250). San Francisco, CA: Morgan Kaufmann .
- De Jong, K. A. (1975). Analysis of behavior of a class of genetic adaptive systems. Unpublished doctoral dissertation, The University of Michigan.
- Denaro, D., & Parisi, D. (1996). Cultural evolution in a population of neural networks. In M. Marinaro & R. Tagliaferri (Eds.), Neural nets wirn-96 (pp. 100111). New York: Springer .
- Fahlmann, S. E. (1991). The recurrent cascade-correlation architecture. In D. S. Touretzky (Ed.), Advances in neural information processing systems 3 (NIPS'91) (pp. 190196). Morgan Kaufmann .
- Floreano, D., & Mondada, F. (1989). Evolution of plastic neurocontrollers for situated agents . Animals to Animats, 4-4 .
- Goldberg, D. E., & Richardson, J. (1987). Genetic algorithms with sharing for multimodal function optimization . In Proceedings of the Second International Conference on Genetic Algorithms and Their Application (pp. 4149 ). Mahwah, NJ: Erlbaum.
- Gruau, F. (1994). Neural network synthesis using cellular encoding and the genetic algorithm. Unpublished doctoral dissertation, Centre d'etude nucleaire de Grenoble, Ecole Normale Superieure de Lyon, France.
- Gusfield, D. (1997). Algorithms on strings, trees and sequences: computer science and computational biology. Cambridge University Press .
- Hillis, W. D. (1990). Co-evolving parasites improve simulated evolution as an optimization procedure . In CNLS '89: Proceedings of the Ninth Annual International Conference of the Center for Nonlinear Studies on Self-Organizing, Collective, and Cooperative Phenomena in Natural and Arti ficial Computing Networks on Emergent Computation (pp. 228234 ). Amsterdam, The Netherlands: North-Holland.
- Hinton, G. E., & Nowlan, S. J. (1987). How learning guides evolution . Complex Systems, 1, 495502 .
- Hutchins, E., & Hazlehurst, B. (1991). Learning in the cultural process. In Artificial life II (pp. 689706). Cambridge, MA: MIT Press .
- Hutchins, E., & Hazlehurst, B. (1995). How to invent a lexicon: The development of shared symbols in interaction. In N. Gilbert & R. Conte (Eds.), Artificial societies: The computer simulation of social life (pp. 157189). London: UCL Press .
- Kawamura, S. (1963). The process of sub-culture propagation among Japanese macaques. Primates Social Behaviour, 8290.
- Kitano, H. (1990). Designing neural networks using genetic algorithm with graph generation system . Complex Systems, 4, 461476 .
- Kolen, J. F., & Pollack, J. B. (1991). Back propagation is sensitive to initial conditions . Advances in Neural Information Processing Systems, 3, 860867 .
- Koza, J. R., & Rice, J. P. (1991, 812). Genetic generation of both the weights and architecture for a neural network . In International Joint Conference on Neural Networks, IJCNN-91 (Vol. II, pp. 397404 ). Seattle, WA: IEEE Computer Society Press.
- MacLennan, B., & Burghardt, G. (1993). Synthetic ethology and the evolution of cooperative communication . Adaptive Behavior, 2, 161188 .[Abstract/Free Full Text]
- Mahfoud, S. W. (1995a). A comparison of parallel and sequential niching methods . In L. J. Eshelman (Ed.), Proceedings of the Sixth International Conference on Genetic Algorithms (pp. 136143 ).
- Mahfoud, S. W. (1995b). Niching methods for genetic algorithms. Unpublished doctoral dissertation, University of Illinois.
- Mandischer, M. (1993). Representation and evolution of neural networks . In R. F. Albrecht, C. R. Reeves, & N. C. Steele (Eds.), Artificial neural nets and genetic algorithms proceedings of the international conference at Innsbruck, Austria (pp. 643649 ). Wien and New York: Springer.
- McPhee, N., & Hopper, N. (1999). Analysis of genetic diversity through population history . In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 11121120 ). Morgan Kaufmann.
- McQuesten, P. H. (2002). Cultural enhancement of neuroevolution. Unpublished doctoral dissertation, University of Texas, Austin.
- Miller, G. F., Todd, P. M., & Hedge, S. U. (1989). Designing neural networks using genetic algorithms . In Proceedings of the Third International Conference on Genetic Algorithms and Their Applications (pp. 379384 ).
- Moriarty, D. E., & Miikkulainen, R. (1995). Discovering complex othello strategies through evolutionary neural networks . Connection Science, 7, 195209 .
- Muhlenbein, H. (1989). Parallel genetic algorithms, population genetics and combinatorial optimization . In Proceedings of the Third International Conference on Genetic Algorithms (pp. 416421 ). San Francisco, CA: Morgan Kaufmann.
- Nolfi, S., & Parisi, D. (1993). Self-selection of input stimuli for improving performance. In Neural networks in robotics (pp. 403418). Kluwer .
- Nolfi, S., & Parisi, D. (1994). Desired answers do not correspond to good teaching inputs in ecological neural networks . Neural processing letters 1 (2), 14 .
- Nolfi, S., & Parisi, D. (1996). Learning to adapt to changing environments in evolving neural networks . Adaptive Behavior, 5, 7597 .
- Nolfi, S., Parisi, D., & Elman, J. L. (1994). Learning and evolution in neural networks . Adaptive Behavior, 3, 528 .[Abstract/Free Full Text]
- O'Reilly, U. M. (1997). Using a distance metric on genetic programs to understand genetic operators . In IEEE International Conference on Systems, Man, and Cybernetics (Vol. 5, pp. 40924097 ).
- Pereira, F. B., & Costa, E. (2001). How learning improves the performance of evolutionary agents: A case study with an information retrieval system for a distributed environment . In Proceedings of the International Symposium on Adaptive Systems: Evolutionary computation and probabilistic graphical models (ISAS 2001) (pp. 1923 ).
- Pujol, J. C. F., & Poli, R. (1998). Efficient evolution of asymmetric recurrent neural networks using a two-dimensional representation . In Proceedings of the First European Workshop on Genetic Programming (EUROGP) (pp. 130141 ).
- Rosca, J. P. (1995). Entropy-driven adaptive representation . In J. P. Rosca (Ed.), Proceedings of the Workshop on Genetic Programming: From theory to real-world applications (pp. 2332 ).
- Sasaki, T., & Tokoro, M. (1997). Adaptation toward changing environments: Why Darwinian in nature? In P. Husbands & I. Harvey (Eds.), Fourth European Conference on Artificial Life (pp. 145153 ). Cambridge, MA: MIT Press.
- Spector, L. (1994). Genetic programming and AI planning systems . In Proceedings of the Twelfth National Conference on Artificial Intelligence (pp. 13291334 ). Seattle, WA: AAAI Press/MIT Press.
- Spector, L., & Luke, S. (1996). Culture enhances the evolvability of cognition . In Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society (pp. 672677 ).
- Spiessens, P., & Manderick, B. (1991). A massively parallel genetic algorithm: Implementation and first analysis. In R. K. Belew & L. B. Booker (Eds.), ICGA (pp. 279287). San Diego, CA: Morgan Kaufmann .
- Steels, L. (1996a). Emergent adaptive lexicons . In P. Maes (Ed.), Proceedings of the Simulation of Adaptive Behavior Conference. Cambridge, MA: MIT Press.
- Steels, L. (1996b). Self-organising vocabularies . In Proceedings of Artificial Life V.
- Steels, L. (1997). The synthetic modeling of language origins. In Evolution of communication (pp. 134).
- Sutton, R. S. (1986). Two problems with backpropagation and other steepest-descent learning procedures for networks . In Proceedings of the 8th Annual Conference of the Cognitive Science Society (pp. 823831 ).
- Watson, J., & Wiles, J. (2002). The rise and fall of learning: A neural network model of the genetic assimilation of acquired traits . In Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002) (pp. 600605 ).
- White, D., & Ligomenides, P. (1993). GANNet: a genetic algorithm for optimizing topology and weights in neural network design . In Proceedings of the International Workshop on Artificial Neural Networks (IWANN'93) (pp. 322327 ). New York: Springer-Verlag.
- Whiten, A., & Ham, R. (1992). On the nature and evolution of imitation in the animal kingdom. In Advances in the study of behaviour, 239283.
- Yanco, H., & Stein, L. (1993). An adaptive communication protocol for cooperating mobile robots . In From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 478485 ). Cambridge, MA: MIT Press.
- Zentall, T. (2001). Imitation in animals: evidence, function and mechanisms . Cybernetics and Systems, 32, 5396 .

CiteULike Connotea Del.icio.us Digg Reddit Technorati What's this?
|