Toward large-scale continuous EDA : a random matrix theory perspective
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 24112695
- Type
- D - Journal article
- DOI
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10.1162/EVCO_a_00150
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 255
- Volume
- 24
- Issue
- 2
- ISSN
- 1063-6560
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- 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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2
- Research group(s)
-
-
- Citation count
- 31
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces random matrix theory to evolutionary search. This enables so-called Estimation of Distribution Algorithms to tame the curse of dimensionality and tackle high (1000) dimensional continuous search problems successfully for the first time. An early version won a Best Paper award at GECCO'13 and follow-on works won a
CEC'15 Runner-up Student Paper Award, and a PPSN'16 Best Paper Nomination. It is significant as it demonstrates that generic high-dimensional phenomena can be turned into solutions of high-dimensional problems. It stimulated much research on scalable evolutionary and stochastic search algorithm development, and also AI work on embedded
bandits (https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/viewPaper/14398).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -