An Adaptive Multi-Population Framework for Locating and Tracking Multiple Optima
- Submitting institution
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Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1199
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2015.2504383
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 590
- Volume
- 20
- Issue
- 4
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- 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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4
- Research group(s)
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B - LOOM
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A key output of an EPSRC project (EP/K001310/1, £445k, 2013-17), this research demonstrates how to improve efficiency in the resolution of dynamic optimisation problems. This “original” research reports “a very significant exploration”, “an extremely thorough description of the new approach”, and “well-executed experiments” (K.C. Tan, Editor, kaytan@cityu.edu.hk). The work resulted in a keynote speech to the international conference MENDEL 2017, an invited tutorial in IEEE CEC2017, and an invited special session in SEAL 2017 (http://www.seal2017.com/SpecialSessions.html). It also led to the development of an open-source framework for evolutionary computation (http://changhe160.github.io/EAlib.html), which has been widely utilised by the research community.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -