Principled design and runtime analysis of abstract convex evolutionary search
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1772
- Type
- D - Journal article
- DOI
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10.1162/EVCO_a_00169
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 205
- Volume
- 25
- Issue
- 2
- ISSN
- 1063-6560
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2017
- URL
-
-
- Supplementary information
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-
- 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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1
- Research group(s)
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-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is a substantial extension to the ground-breaking Geometric Theory of Evolutionary Algorithms (https://dl.acm.org/doi/10.1145/2330163.2330255 BEST PAPER AT GECCO), founded by the author and regularly covered at invited tutorials at major conferences (GECCO/PPSN/CEC 2011-2020), which unifies Evolutionary Algorithms across representations and has been used for their rigorous runtime analysis and principled design of new successful search algorithms. This extension has been applied on an ongoing collaboration with Fujitsu Research Laboratories to analyse optimisation algorithms and problem landscapes (email: serban.georgescu@fujitsu.com) and forms the basis of two recently completed theoretical PhD theses -Marcos Diez Garcia (problem landscapes) and Tina Malalanirainy (runtime analysis)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -