A Case Study of Controlling Crossover in a Selection Hyper-heuristic Framework Using the Multidimensional Knapsack Problem
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1321840
- Type
- D - Journal article
- DOI
-
10.1162/EVCO_a_00145
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 113
- Volume
- 24
- Issue
- 1
- ISSN
- 1063-6560
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- 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
- Yes
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This pioneering interdisciplinary study investigates the maintenance and control of input arguments to a binary operator (crossover) in two separate layers of a single-point-based search hyper-heuristic. The performance comparison of various hyper-heuristic optimisers embedding exact and inexact methods is significant, testing across all three well-known benchmark libraries of the Multidimensional Knapsack Problem for the first time . Novel insights and new lower bounds for some problem instances obtained from a commercial MIP solver are provided. Benchmarks were released via ResearchGate, receiving about 3000 reads. This work was produced from the research activities of the GBP5.4M EPSRC-funded LANCS initiative.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
