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.
KeywordsVirtual Machine Cellular Automaton Cellular Automaton Random Search Knowledge Diffusion
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