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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| Published in: | International Workshop on Software Engineering for Adaptive and Self-Managing Systems (SEAMS '07) p. 13 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
Washington, DC, USA
IEEE Computer Society
20.05.2007
IEEE |
| Series: | ACM Conferences |
| Subjects: | |
| ISBN: | 0769529739, 9780769529738 |
| ISSN: | 2157-2305 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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 self-replicating 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. |
| Author | Ofria, Charles A. Cheng, Betty H. C. McKinley, Philip K. Goldsby, Heather J. Knoester, David B. |
| Author_xml | – sequence: 1 givenname: Heather J. surname: Goldsby fullname: Goldsby, Heather J. organization: Michigan State University – sequence: 2 givenname: David B. surname: Knoester fullname: Knoester, David B. organization: Michigan State University – sequence: 3 givenname: Betty H. C. surname: Cheng fullname: Cheng, Betty H. C. organization: Michigan State University – sequence: 4 givenname: Philip K. surname: McKinley fullname: McKinley, Philip K. organization: Michigan State University – sequence: 5 givenname: Charles A. surname: Ofria fullname: Ofria, Charles A. organization: Michigan State University |
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| DOI | 10.1109/SEAMS.2007.6 |
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| SubjectTerms | 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 |
| Title | Digitally Evolving Models for Dynamically Adaptive Systems |
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