- Artificial Neural Networks
- Evolutionary Algorithms
- Swarm Intelligence
- Harmony Search
- Simulated Annealing
- Membrane Computing
- Artificial Immune System (AIS)
- DNA Computation
- Computing with Words
- Artificial Life
- Quantum Computation
- Hybrid Approaches
If All the above features can be combined and made effective implementation on a real-time basis which should provide the right information at the right time can be said as NIMI (Nature Inspired Machine Intelligence). No matter who develops, any R&D division of a small company to Microsoft, Infosys, TCS, Wipro can emerge as a indisputable common brand in common mans day to day life. I say it because I believe technology has to be cheaper and should always reach common man as a help. What is the future of burning carbon and making a global footprint on Ozone layer? To avoid education and methodologies has to evolve from basic to basic+1 at least.
References[1] A. Abraham, “Neuro-Fuzzy Systems: State-of-the-Art Modeling Techniques”, in Jose Mira and Alberto Prieto, eds., Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, Springer Verlag Germany, 2001, pp. 269-276.
[2] W. Banzhaf, P. Nordin, E.R. Keller, and F.D. Francone, “Genetic Programming: An Introduction on The Automatic Evolution of Computer Programs and its Applications”, Morgan Kaufmann Publishers, Inc., 1998
[3] Kirkpatrick, S., C. D. Gelatt Jr., M. P. Vecchi, Optimization by Simulated Annealing, Science, 220, 4598, 671-680, 1983.
[4] G. Paun, Computing with membranes, Journal of Computer and System Sciences, 61 (1), 108-143, 2000.
[5] Deutsch, D., Quantum Theory, the Church-Turing Principle, and the Universal Quantum Computer”. Proc. Roy. Soc. Lond. A400, 97–117, 1985.
[6] A. Abraham, Intelligent Systems: Architectures and Perspectives, Recent Advances in Intelligent Paradigms and Applications, Abraham A., Jain L. and Kacprzyk J. (Eds.), Studies in Fuzziness and Soft Computing, Springer Verlag Germany, ISBN 3790815381, Chapter 1, pp. 1-35, 2002.
[7] Bishop C.M., Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995.
[8] Fogel, D. B., Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ, Second edition, 1999.
[9] Kennedy J. and Eberhart R. Swarm intelligence. Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2001.
[10] Passino, K.M., Biomimicry of Bacterial Foraging for Distributed Optimization and Control, IEEE Control Systems Magazine, pp. 52-67, June 2002.
[11] de Castro, L. N. and Timmis, J. I., Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag, London, 2002.
[12] Amos M., Theoretical and Experimental DNA Computation. Springer, ISBN: 3-540-65773-8, 2005.
[13] Zadeh L.A. and Kacprzyk J. (Eds.) Computing with Words in Information/Intelligent Systems: Foundations, Studies in Fuzziness and Soft Computing, Springer Verlag, Germany, ISBN 379081217X, 1999.
[14] Reynolds R.G., Michalewicz, Z. Cavaretta M.J., Using Cultural Algorithms for Constraint Handling in GENOCOP. Proceedings of the Fourth Annual Conference on Evolutionary Programming. MIT Press, Cambridge, pp. 289-305, 1995.
[15] C. Adami, Introduction to Artificial Life. Springer-Verlag New York, Inc., 1998.
[16] Z.W. Geem, J.H. Kim, and G.V. Loganathan, “A new heuristic optimization algorithm: harmony search”, Simulation 76 (2), 60–68, 2001.
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