Coevolutionary systems and PageRank
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 91356566
- Type
- D - Journal article
- DOI
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10.1016/j.artint.2019.103164
- Title of journal
- Artificial Intelligence
- Article number
- 103164
- First page
- -
- Volume
- 277
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper unveils a deep connection between co-evolutionary systems and the well-known and studied PageRank. This is important since theoretical analysis of co-evolutionary systems is much more challenging than (and lags behind) that of evolutionary ones, where a clear fixed measure of solution quality exists. Yet, co-evolutionary systems have been successfully used in various problem domains involving situations of strategic decision-making. We derive closed-form expressions for PageRank authorities of solutions and show how they can be approximated effectively. This opens the door for development of a new theory of measuring and ranking the solution performance.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -