Optimisation of a Digital Ecosystem by a Distributed Intelligence

Can intelligence optimise Digital Ecosystems? How could a distributed intelligence interact with the ecosystem dynamics? Can the software components that are part of genetic selection be intelligent in themselves, as in an adaptive technology? We consider the effect of a distributed intelligence mechanism on the evolutionary and ecological dynamics of our Digital Ecosystem, which is the digital counterpart of a biological ecosystem for evolving software services in a distributed network. We investigate Neural Networks and Support Vector Machine for the learning based pattern recognition functionality of our distributed intelligence. Simulation results imply that the Digital Ecosystem performs better with the application of a distributed intelligence, marginally more effectively when powered by Support Vector Machine than Neural Networks, and suggest that it can contribute to optimising the operation of our Digital Ecosystem.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[3]  Ahmed K. Elmagarmid,et al.  Composing Web services on the Semantic Web , 2003, The VLDB Journal.

[4]  Jos de Bruijn,et al.  The Web Service Modeling Language WSML: An Overview , 2006, ESWC.

[5]  Nixon,et al.  Feature Extraction & Image Processing , 2008 .

[6]  Richard T. Gillam Unicode Demystified: A Practical Programmer's Guide to the Encoding Standard , 2002 .

[7]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[8]  Leonid I. Perlovsky,et al.  Conundrum of Combinatorial Complexity , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  B. Weber,et al.  Evolution and Learning: The Baldwin Effect Reconsidered , 2003 .

[11]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[12]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[13]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[14]  Deborah L. McGuinness,et al.  Bringing Semantics to Web Services: The OWL-S Approach , 2004, SWSWPC.

[15]  J. Baldwin A New Factor in Evolution , 1896, The American Naturalist.

[16]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[17]  Schahram Dustdar,et al.  A survey on web services composition , 2005, Int. J. Web Grid Serv..

[18]  Terence Soule,et al.  Effects of Code Growth and Parsimony Pressure on Populations in Genetic Programming , 1998, Evolutionary Computation.

[19]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[20]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[21]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[22]  Douglas R. White,et al.  The Navigability of strong ties: small worlds, tie strength, and network topology , 2002 .

[23]  Nick Collier,et al.  Repast: An extensible framework for agent simulation , 2001 .

[24]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[25]  R. Trivers The Evolution of Reciprocal Altruism , 1971, The Quarterly Review of Biology.

[26]  Erik D. Goodman,et al.  Coarse-grain parallel genetic algorithms: categorization and new approach , 1994, Proceedings of 1994 6th IEEE Symposium on Parallel and Distributed Processing.

[27]  S. Elena,et al.  The evolution of sex: empirical insights into the roles of epistasis and drift , 2007, Nature Reviews Genetics.

[28]  E. Lawrence Henderson's Dictionary of Biological Terms , 1975 .

[29]  Adam Blum,et al.  Neural Networks in C++: An Object-Oriented Framework for Building Connectionist Systems , 1992 .

[30]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[31]  Jeffrey E. F. Friedl Mastering Regular Expressions , 1997 .

[32]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[33]  King-Sun Fu,et al.  Syntactic Pattern Recognition And Applications , 1968 .

[34]  Lothar Thiele,et al.  A Comparison of Selection Schemes Used in Evolutionary Algorithms , 1996, Evolutionary Computation.

[35]  Maha Shaikh Open philosophies for associative autopoietic digital ecosystems (OPAALS) , 2008 .

[36]  Amit P. Sheth,et al.  Enhancing Web Services Description and Discovery to Facilitate Composition , 2004, SWSWPC.

[37]  M. Begon,et al.  Ecology: Individuals, Populations and Communities , 1986 .

[38]  Joachim Stender,et al.  Parallel Genetic Algorithms: Theory and Applications , 1993 .

[39]  X. Yang,et al.  Chaos in small-world networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[41]  Riccardo Poli,et al.  Parallel genetic algorithm taxonomy , 1999, 1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410).

[42]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[43]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[44]  F. Valafar Pattern Recognition Techniques in Microarray Data Analysis , 2002, Annals of the New York Academy of Sciences.