The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Borg: An Auto-Adaptive Many-Objective Evolutionary Computing Framework
Abstract
This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines [Formula: see text]-dominance, a measure of convergence speed named [Formula: see text]-progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm's feasible parameteri- zation space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.
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A Knee Point-Driven Evolutionary Algorithm for Many-Objective Optimization
Zhang, X., Tian, Y., & Jin, Y. (2015) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2014.2378512 · Cited by 867
Maier, H. R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L. S., Cunha, M. C., Dandy, G. C., Gibbs, M. S., Keedwell, E., Marchi, A., et al. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.09.013 · Cited by 657
Self-citationBio-inspired computation: Where we stand and what's next
Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.008 · Cited by 593
Review of numerical optimization techniques for meta-device design [Invited]
Campbell, S. D., Sell, D., Jenkins, R. P., Whiting, E. B., Fan, J. A., & Werner, D. H. (2019) · Optical Materials Express · doi:10.1364/ome.9.001842 · Cited by 322
Metadata
- Authors
- Hadka, D., & Reed, P.
- Venue
- Evolutionary Computation
- Publisher
- MIT Press - Journals
- Published
- 2013-05
- Volume
- 21
- Issue
- 2
- Pages
- 231-259
- ISSN
- 1063-6560 (ISSN-L)
- 1530-9304
- OpenAlex
- W2167757882
- Cited by
- 717
- References
- 75
- FWCI
- 30.88
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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