|dc.description.abstract||Increasing complexity of software applications forces researchers to look for automated ways of programming and adapting these systems. Self-adapting, self-organising software system is one of the possible ways to tackle and manage higher complexity. A set of small independent problem solvers, working together in a dynamic environment, solving multiple tasks, and dynamically adapting to changing requirements is one way of achieving true self-adaptation in software systems. Our work presents a dynamic multi-task environment and experiments with a self-adapting software system. The Evolvable Virtual Machine (EVM) architecture is a model for building complex hierarchically organised software systems. The intrinsic properties of EVM allow the independent programs to evolve into higher levels of complexity, in a way analogous to multi-level, or hierarchical evolutionary processes. The EVM is designed to evolve structures of self-maintaining, self-adapting ensembles, that are open-ended and hierarchically organised. This article discusses the EVM architecture together with different statistical exploration methods that can be used with it. Based on experimental results, certain behaviours that exhibit self-adaptation in the EVM system are discussed.||en_NZ
|dc.description.references||Manfred Eigen and Peter Schuster. The Hypercycle: A Principle of Natural Self-Organization. Springer-Verlag, 1979.
David B. Fogel, editor. Evolutionary Computation — The Fossil Record. IEEE Press, New York, USA, 1998.
John R. Koza, Forrest H. Bennett, David Andre, and Martin A. Keane. Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann Publishers, 1999.
Leonid A. Levin. Universal sequential search problems. Problems of Information Transmission, 9(3):265–266, 1973.
Lynn Margulis. Origin of Eukaryotic Cells. University Press, New Haven, 1970.
Lynn Margulis. Symbiosis in Cell Evolution. Freeman & Co., San Francisco, 1981.
Humberto R. Maturana and Francisco J. Varela. Autopoiesis: The organization of the living. In Robert S. Cohen and Marx W. Wartofsky, editors, Autopoiesis and Cognition: The Realization of the Living, volume 42 of Boston Studies in the Philosophy of Science. D. Reidel Publishing Company, Dordrech, Holland, 1980.
Konstantin Sergeivich Mereschkowsky. Über Natur und Ursprung der Chromatophoren im Pflanzenreiche. Biol. Zentralbl., 25:593–604, 1905.
Mariusz Nowostawski, Martin Purvis, and Stephen Cranefield. An architecture for self-organising evolvable virtual machines. In Sven Brueckner, Giovanna Di Marzo Serugendo, Anthony Karageorgos, and Radhika Nagpal, editors, Engineering Self Organising Sytems: Methodologies and Applications, number 3464 in Lecture Notes in Artificial Intelligence. Springer Verlag, 2004.
Juergen Schmidhuber. A general method for incremental self-improvement and multiagent learning. In X. Yao, editor, Evolutionary Computation: Theory and Applications, chapter 3, pages 81–123. Scientific Publishers Co., Singapore, 1999.
Juergen Schmidhuber. Optimal ordered problem solver. Machine Learning, 54:211–254, 2004.
René Thom. Structural stability and morphogenesis. Benjamin Addison Wesley, New York, 1975.
Michael D. Vose. The Simple Genetic Algorithm: Foundations and Theory. A Bradford Book, MIT Press, Cambridge, Massachusetts/London, England, 1999.
Ivan Wallin. Symbionticism and the Origin of Species. Williams & Wilkins, Baltimore, 1927.
Stephen Wolfram. Universality and complexity in cellular automata. Physica D, 10:1–35, 1984.
Stephen Wolfram. A New Kind of Science. Wolfram Media, Inc., first edition, May 2002.
Sewall Wright. Evolution in mendelian populations. Genetics, 16(3):97–159, March 1931.||en_NZ