Self-adaptation and Dynamic Environment Experiments with Evolvable Virtual Machines

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.

[1]  I. E. Wallin,et al.  Symbionticism and the origin of species / by Ivan E. Wallin. , 1927 .

[2]  Stephen Wolfram,et al.  A New Kind of Science , 2003, Artificial Life.

[3]  Martin K. Purvis,et al.  An Architecture for Self-Organising Evolvable Virtual Machines , 2004, Engineering Self-Organising Systems.

[4]  David B. Fogel,et al.  Evolutionary Computation: The Fossil Record , 1998 .

[5]  Robert Rosen,et al.  Structural stability and morphogenesis , 1977 .

[6]  W. Martin,et al.  ANNOTATED ENGLISH TRANSLATION OF MERESCHKOWSKY'S 1905 PAPER 'UBER NATUR UND URSPRUNG DER CHROMATOPHOREN IM PFLANZENREICHE' , 1999 .

[7]  P. Frank,et al.  Boston Studies in the Philosophy of Science , 1968 .

[8]  Jürgen Schmidhuber,et al.  Optimal Ordered Problem Solver , 2002, Machine Learning.

[9]  Simon K.A. Robson,et al.  Book review of "The origin of sex: three billion years of genetic recombination" by Lynn Margulis and Dorion Sagan, Yale University Press, New Haven and London , 1987 .

[10]  H. Maturana,et al.  Autopoiesis and Cognition : The Realization of the Living (Boston Studies in the Philosophy of Scie , 1980 .

[11]  John R. Koza.  Genetic Programming III - Darwinian Invention and Problem Solving , 1999, Evolutionary Computation.

[12]  Betsey Dexter Dyer,et al.  The origin of eukaryotic cells , 1985 .

[13]  John R. Koza,et al.  Genetic Programming III: Darwinian Invention & Problem Solving , 1999 .

[14]  P. Raven,et al.  ORIGIN OF EUKARYOTIC CELLS , 1971 .

[15]  Michael D. Vose,et al.  The simple genetic algorithm - foundations and theory , 1999, Complex adaptive systems.

[16]  Corso Elvezia.  A General Method for Incremental Self-improvement and Multi-agent Learning in Unrestricted Environments , 1996 .

[17]  L. Margulis.  Symbiosis in cell evolution: Life and its environment on the early earth , 1981 .

[18]  Salima Hassas,et al.  Self-Organisation: Paradigms and Applications , 2003, Engineering Self-Organising Systems.

[19]  H. Maturana,et al.  Autopoiesis and Cognition , 1980 .

[20]  I. E. Wallin.  Symbionticism and the Origin of Species , 1927, The Indian Medical Gazette.

[21]  Stephen Wolfram,et al.  Universality and complexity in cellular automata , 1983 .

[22]  W. Schwartz,et al.  L. Margulis, Symbiosis in Cell Evolution. Life and its Environment on the Early Earth. XXII + 479 S., 104 Abb., 59 Tab. San Francisco 1981. W. H. Freeman and Co. £ 16.40 (Board), £ 9.20 (Paper) , 1982 .

[23]  M. Eigen,et al.  The Hypercycle: A principle of natural self-organization , 2009 .

[24]  Juergen Schmidhuber,et al.  A General Method For Incremental Self-Improvement And Multi-Agent Learning In Unrestricted Environme , 1999 .

[25]  Xin Yao,et al.  Evolutionary computation : theory and applications , 1999 .

[26]  S. Wright,et al.  Evolution in Mendelian Populations. , 1931, Genetics.