Abstract
Several general evolutionary approaches have proven quite successful at evolving teams (or ensembles) consisting of cooperating team members. However, in this paper we demonstrate that the existing approaches have subtle, but significant, weaknesses. We then present a novel class of evolutionary algorithms (orthogonal evolution of teams (OET)) for evolving teams that overcomes these weaknesses. Specifically it is shown that a typical algorithm from the OET class of algorithms successfully generates team members that have fitnesses comparable to those evolved independently and that have inversely correlated errors, which maximizes the teams’ overall performance. Finally it is shown that the OET approach performs significantly better than the standard evolutionary approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Avizienis, A. and Kelly, J. B. J. (1984). Fault tolerance by design diversity: Concepts and experiments. In IEEE Computer, volume 17(8), pages 67–80.
Brameier, Markus and Banzhaf, Wolfgang (2001). Evolving teams of predictors with linear genetic programming. Genetic Programming and Evolvable Machines, 2(4):381–408.
Breiman, L. (1994). Bagging predictor, technical report 421. Technical report, University of California Berkley.
Cantu-Paz, Erick and Kamath, Chandrika (2003). Inducing oblique decision trees with evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 7(1):54–68.
Feldt, R. (1998). Generating multiple diverse software versions with genetic programming. In Proceedings of the 24th EUROMICRO Conference, Workshop on Dpendable Computing Systems, pages 387–396.
Hatton, L. (1997). N-version vs. one good program. In IEEE Software, volume 14(6), pages 71–76.
Haynes, Thomas, Sen, Sandip, Schoenefeld, Dale, and Wainwright, Roger (1995). Evolving a team. In Siegel, E. V. and Koza, J. R., editors, Working Notes for the AAAI Symposium on Genetic Programming, pages 23–30, MIT, Cambridge, MA, USA. AAAI.
Hilford, V., Lyu, M. R., Cukie, B., Jamoussi, A., and Bastani, F. B. (1997). Diversity in the software development process. In Proceedings of WORDS’97.
Iba, Hitoshi (1997). Multiple-agent learning for a robot navigation task by genetic programming. In Koza, John R., Deb, Kalyanmoy, Dorigo, Marco, Fogel, David B., Garzon, Max, Iba, Hitoshi, and Riolo, Rick L., editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 195–200, Stanford University, CA, USA. Morgan Kaufmann.
Iba, Hitoshi (1999). Bagging, boosting, and bloating in genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference: GECCO-1999, pages 1053–1060. Morgan Kaufmann.
Imamura, Kosuke, Heckendorn, Robert B., Soule, Terence, and Foster, James A. (2002). N-version genetic programming via fault masking. In Foster, James A., Lutton, Evelyne, Miller, Julian, Ryan, Conor, and Tettamanzi, Andrea G. B., editors, Genetic Programming, Proceedings of the 5th European Conference, EuroGP 2002, volume 2278 of LNCS, pages 172–181, Kinsale, Ireland. Springer-Verlag.
Imamura, Kosuke, Soule, Terence, Heckendorn, Robert B., and Foster, James A. (2003). Behavioral diversity and a probabilistically optimal GP ensemble. Genetic Programming and Evolvable Machines, 4(3):235–253.
Knight, J. C. and Leveson, N. B. (1986). An experimental evaluation of the assumption of independence in multiversion programming. In IEEE Transactions on Software Engineering, volume 12.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (ICJA), pages 1137–1145. Morgan Kaufmann.
Koza, John (1992). A genetic approach to the truck backer upper problem and the inter-twined spiral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, pages 310–318. IEEE Press.
Liu, Yong, Yao, Xin, and Higuchi, Tetsuya (2000). Evolutionary ensembles with negative correlation learning. IEEE Transactions on Evolutionary Computation, 4(4):380–387.
Luke, Sean and Spector, Lee (1996). Evolving teamwork and coordination with genetic programming. In Koza, John R., Goldberg, David E., Fogel, David B., and Riolo, Rick L., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 150–156, Stanford University, CA, USA. MIT Press.
Maclin, R. and Optiz, D. (1999). An empirical evaluation of bagging and boosting. In Proceedings of the 14th International Conference on Artificial Intelligence, pages 546–551. AAAI Press/MIT Press.
Maqsood, Imran, Khan, Muhammad Raiz, and Abraham, Ajith (2004). An ensemble of neural networks for weather prediction. Neural Computing and Applications, 13(2):112–123.
Obitz, D. W., Basak, S. C., and Gute, B. D. (1999). Hazard assessment modeling: An evolutionary ensemble approach. In Proceedings of the Genetic and Evolutionary Computation Conference: GECCO-1999, pages 1543–1650. Morgan Kaufmann.
Peng, K., Vucetic, S., Radivojac, P., Brown, C.J., Dunker, A.K., and Obradovic, Z. (2004). Optimizing long intrinsic disorder predictors with protein evolutionary information. Journal of Bioinformatics and Computational Biology, 3(1):1–26.
Platel, Michael Defoin, Chami, Malik, Clergue, Manuel, and Collard, Philippe (2005). Teams of genetic predictors for inverse problem solving. In Proceeding of the 8th European Conference on Genetic Programming-EuroGP 2005.
Raik, Simon and Durnota, Bohdan (1994). The evolution of sporting strategies. In Stonier, Russel J. and Yu, Xing Huo, editors, Complex Systems: Mechanisms of Adaption, pages 85–92. IOS Press.
Schapire, R. E. and Freund, Y. (1999). A short introduction to boosting. In Journal of the Japanese Society for Artificial Intelligence, volume 14(5), pages 771–780.
Soule, Terence (1999). Voting teams: A cooperative approach to non-typical problems. In Banzhaf, Wolfgang, Daida, Jason, Eiben, Agoston E., Garzon, Max H., Honavar, Vasant, Jakiela, Mark, and Smith, Robert E., editors, Proceedings of the Genetic and Evolutionary Computation Conference, pages 916–922, Orlando, Florida, USA. Morgan Kaufmann.
Soule, Terence (2000). Heterogeneity and specialization in evolving teams. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 778–785, Las Vegas, Nevada, USA. Morgan Kaufmann.
Soule, Terence (2003). Cooperative evolution on the intertwined spirals problem. In Genetic Programming: Proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, pages 434–442. Springer-Verlag.
Widmer, Gerhard (2003). Discovering simple rules in complex data: a meta-learning algorithm and some surprising musical discoveries. Artificial Intelligence, 146:129–148.
Zang, B. T. and Joung, J. G. (1997). Enhancing robustness of genetic programming at the species level. In Proceedings of the 2nd Annual Conference on Genetic Programming, pages 336–342. Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Soule, T., Komireddy, P. (2007). Orthogonal Evolution of Teams: A Class of Algorithms for Evolving Teams with Inversely Correlated Errors. In: Riolo, R., Soule, T., Worzel, B. (eds) Genetic Programming Theory and Practice IV. Genetic and Evolutionary Computation. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-49650-4_6
Download citation
DOI: https://doi.org/10.1007/978-0-387-49650-4_6
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-33375-5
Online ISBN: 978-0-387-49650-4
eBook Packages: Computer ScienceComputer Science (R0)