Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Mohr, Philipp H.; Ryan, Nick S.; Timmis, Jon (2004)
Publisher: Springer
Languages: English
Types: Unknown
Subjects: QA76

Classified by OpenAIRE into

mesheuropmc: biochemical phenomena, metabolism, and nutrition
ACM Ref: TheoryofComputation_MISCELLANEOUS
We propose that bio-inspired algorithms are best developed and analysed in the context of a multidisciplinary conceptual framework that provides for sophisticated biological models and well-founded analytical principles, and we outline such a framework here, in the context of AIS network models. We further propose ways to unify several domains into a common meta-framework, in the context of AIS population models. We finally hint at the possibility of a novel instantiation of such a meta-framework, thereby allowing the building of a specific computational framework that is inspired by biology, but not restricted to any one particular biological domain.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Bäck, T. Evolutionary Algorithms in Theory and Practice. 1996. Oxford University Press, New York.
    • 2. Bentley, P. J. Fractal Proteins. 2004. In Genetic Programming and Evolvable Machines Journal.
    • 3. Bentley, P. J. Evolving Fractal Gene Regulatory Networks for Robot Control. 2003a. In Proceedings of ECAL 2003.
    • 4. Bentley, P. J. Evolving Beyond Perfection: An Investigation of the Effects of Long-Term Evolution on Fractal Gene Regulatory Networks. 2003b. In Proc of Information Processing in Cells and Tissues (IPCAT 2003).
    • 5. De Castro, L.N and Timmis, J (2002a). “An Artificial Immune Network for multi-modal optimisation”. In proceedings of IEEE World Congress on Computational Intelligenece. Pp. 699-704.
    • 6. De Castro, L.N and Timmis, J (2002b). “Artificial Immune Systems: A New Computational Intelligence Approach”. Springer-Verlag.
    • 7. de Castro, L. N. & Von Zuben, F. J. (2001), “aiNet: An Artificial Immune Network for Data Analysis”, in Data Mining: A Heuristic Approach, H. A. Abbass, R. A. Sarker, and C. S. Newton (eds.), Idea Group Publishing, USA, Chapter XII, pp. 231-259.
    • 8. Farmer, J. D., Packard, N. H. & Perelson, A. S. (1986), “The Immune System, Adaptation, and Machine Learning”, Physica 22D, pp. 187-204.
    • 9. Jerne, N. K. (1974), “Towards a Network Theory of the Immune System”, Ann. Immunol. (Inst. Pasteur) 125C, pp. 373-389.
    • 10. Kumar, S. and Bentley, P. J.. Computational Embryology: Past, Present and Future. 2003. Invited chapter in Ghosh and Tsutsui (Eds) Theory and Application of Evolutionary Computation: Recent Trends. Springer Verlag (UK).
    • 11. Mandelbrot, B. The Fractal Geometry of Nature. 1982. W.H. Freeman & Company.
    • 12. Neal, M. (2003), “Meta-stable Memory in an Artificial Immune Network”, LNCS 2787. pp. 168-180. Timmis, J, Bentley, P and Hart E. (Eds). Springer-Verlag.
    • 13. Perelson, A. S. (1989), “Immune Network Theory”, Imm. Rev., 110, pp. 5-36.
    • 14. Perelson, A. S. & Oster, G. F. (1979), “Theoretical Studies of Clonal Selection: Minimal Antibody Repertoire Size and Reliability of Self-Nonself Discrimination”, J. theor.Biol., 81, pp. 645-670.
    • 15. Timmis, J. and Neal, M (2001). “A resource limited artificial immune system for data analysis” Knowledge Based Systems. 14(3-4).:121-130.
    • 16. Wolpert, L., Rosa Beddington, Thomas Jessell, Peter Lawrence, Elliot Meyerowitz, Jim Smith. Principles of Development, 2nd Ed. 2001. Oxford University Press.
    • 1. N. Good, J. Schafer, J Konstan, A Borchers, B Sarwar, J Herlocker, J. Riedl: Combining collaborative filtering with personal agents for better recommendations, in proceedings of the sixteenth national conference on artificial intelligence (1999)
    • 2. Xiaobin Fu, J.B., Hammond, K.J.: Mining navigation history for recommendation, in proc. 2000 conf. on intelligent user interfaces (2000)
    • 3. Abowd, G.D., Dey, A.K.: Towards a better understanding of context and contextawareness, http://www.cc.gatech.edu/fce/contexttoolkit/chiws/dey.pdf (2000)
    • 4. Kim, J., Bentley, P.: The human immune system and network intrusion detection (1999)
    • 5. Secker, A., Freitas, A., Timmis, J.: AISEC: An Artificial Immune System for Email Classification. In Sarker, R., Reynolds, R., Abbass, H., Kay-Chen, T., McKay, R., Essam, D., Gedeon, T., eds.: Proceedings of the Congress on Evolutionary Computation, Canberra. Australia, IEEE (2003) 131-139
    • 6. Weiser, M.: The computer for the 21st century. Scientific American (1991)
    • 7. Ling Bao, Stephen S. Intille: Activity recognition from user-annotated acceleration data, in Proceedings of Pervasive Computing 2004,Linz/Vienna,Austria. (2004)
    • 8. Orr, R., Abowd, G.: The smart floor: A mechanism for natural user identification and tracking (2000)
    • 9. Jeffrey Hightower, G.B.: Location systems for ubiquitous computing. IEEE Computer 38 (2001) 57-66
    • 10. Patrik Osbakk, Nick Ryan: A privacy enhancing infrastructure for contextawareness, position paper for the 1st UK-ubinet Workshop, Imperial College, London, UK (2003)
    • 11. Kiyoharu Aizawa, Tetsuro Hori, Shinya Kawasaki, Takayuki Ishikawa: Capture and efficient retrieval of life log, in Proceedings of Pervasive Computing 2004 workshop on memory and sharing of experiences,Linz/Vienna,Austria. (2004)
    • 12. Ashbrook, D., Starner, T.: Learning significant locations and predicting user movement with gps, proceedings of ieee sixth international symposium on wearable computing (iswc02) (2002)
    • 13. M. C. Mozer: The neural network house: An environment that adapts to its inhabitants. in Proceedings of the AAAI 1998 Spring Symposium on Intelligent Environments (1998)
    • 14. Alexander Kroener, Stephan Baldes, Anthony Jameson and Mathias Bauer: Using an extended episodic memory within a mobile companion (2004)
    • 15. Rene Mayerhofer, Harald Radi, Alois Ferscha: Recognizing and predicting context by learning from user behavior, in Proceedings of The International Conference On Advances in Mobile Multimedia (MoMM2003),Austrian Computer Society (OCG) (2003)
    • 16. Jerne, N.: Towards a network theory of the immune system. Ann. Immunol (1979)
    • 17. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation and machine learning. Phsica 22 (1986) 187-204
    • 18. de Castro, L., Timmis, J.: Artificial Immune Systems: A New Computational Approach. Springer-Verlag, London. UK. (2002)
    • 19. Neal, M.: Meta-stable Memory in an Artificial Immune Network. In Timmis, J., Bentley, P., Hart, E., eds.: Proceedings of the 2nd International Conference on Artificial Immune Systems. Volume 2787 of Lecture Notes in Computer Science., Springer (2003) 229-241
    • 20. Timmis, J., Neal, M.: A resource limited artificial immune system for data analysis. Knowledge Based Systems 14 (2001) 121-130
    • 21. Neal, M.: An artificial immune system for continuous analysis of time-varying data. In Timmis, J., Bentley, P.J., eds.: Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS). Volume 1., University of Kent at Canterbury, University of Kent at Canterbury Printing Unit (2002) 76-85
    • 22. Jan Petzold, Faruk Bagci, W.T., Theo Ungerer, i.A.I.i.M.S..: Global and local state context prediction (2003)
    • 23. Freitas, A., Timmis, J.: Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective. In Timmis, J., Bentley, P., Hart, E., eds.: Proceedings of the 2nd International Conference on Artificial Immune Systems. Volume 2787 of Lecture Notes in Computer Science., Springer (2003) 229-241
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article