Digitally Evolving Models for Dynamically Adaptive Systems

Developing a Dynamically Adaptive System (DAS) requires a developer to identify viable target systems that can be adopted by the DAS at runtime in response to specific environmental conditions, while satisfying critical properties. This paper describes a preliminary investigation into using digital...

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Bibliographic Details
Published in:International Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS '07) p. 13
Main Authors: Goldsby, Heather J., Knoester, David B., Cheng, Betty H. C., McKinley, Philip K., Ofria, Charles A.
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 20.05.2007
IEEE
Series:ACM Conferences
Subjects:
Adaptive systems
Computer aided instruction
Computer science
Computing methodologies > Machine learning > Machine learning approaches > Neural networks
Condition monitoring
Evolution (biology)
Genetic mutations
Hardware > Emerging technologies > Circuit substrates > Cellular neural networks
Organisms
Runtime environment
Software and its engineering > Software creation and management > Software post-development issues > Software reverse engineering
Software and its engineering > Software organization and properties > Software system structures > Software architectures
Space exploration
Theory of computation > Theory and algorithms for application domains > Machine learning theory
Unified modeling language
ISBN:0769529739, 9780769529738
ISSN:2157-2305
Online Access:Get full text
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Summary:Developing a Dynamically Adaptive System (DAS) requires a developer to identify viable target systems that can be adopted by the DAS at runtime in response to specific environmental conditions, while satisfying critical properties. This paper describes a preliminary investigation into using digital evolution to automatically generate models of viable target systems. In digital evolution, a population of selfreplicating computer programs exists in a user-defined computational environment and is subject to instruction-level mutations and natural selection. These "digital organisms" have no built-in ability to generate a model - each population begins with a single organism that only has the ability to self-replicate. In a case study, we demonstrate that digital evolution can be used to evolve known state diagrams and to further evolve these diagrams to satisfy system critical properties. This result shows that digital evolution can be used to aid in the discovery of the viable target systems of a DAS.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
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ISBN:0769529739
9780769529738
ISSN:2157-2305
DOI:10.1109/SEAMS.2007.6