Self-adaptive learning for hybrid genetic algorithms
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 21215914
- Type
- D - Journal article
- DOI
-
10.1007/s12065-020-00425-5
- Title of journal
- Evolutionary Intelligence
- Article number
- 0
- First page
- 0
- Volume
- 0
- Issue
- -
- ISSN
- 1864-5909
- Open access status
- Compliant
- Month of publication
- May
- Year of publication
- 2020
- 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
-
2
- Research group(s)
-
B - Computational Intelligence
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Memetic algorithms are genetic algorithms hybridised with local search. An efficient memetic algorithm is presented that circumvents manual tuning of its parameters and continues to tune them dynamically during evolution. Prior to this work, there was no model to determine the search strategy, i.e., the mix of Baldwinian and Lamarckian approaches. Here, the strategy is optimised during evolution by including chromosomes that represent the hybrid strategy.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -