The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Multi-objective optimization using genetic algorithms: A tutorial
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Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 10,109
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,798
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Zitzler, E., Deb, K., & Thiele, L. (2000) · Evolutionary Computation · doi:10.1162/106365600568202 · Cited by 5,697
Cited by
A review on simulation-based optimization methods applied to building performance analysis
Nguyen, A.-T., Reiter, S., & Rigo, P. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.08.061 · Cited by 1,379
Individual dispersal, landscape connectivity and ecological networks
Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M., & Turlure, C. (2013) · Biological Reviews · doi:10.1111/brv.12000 · Cited by 714
Multiobjective firefly algorithm for continuous optimization
Yang, X.-S. (2013) · Engineering with Computers · doi:10.1007/s00366-012-0254-1 · Cited by 513
Multiobjective Reinforcement Learning: A Comprehensive Overview
Liu, C., Xu, X., & Hu, D. (2015) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2014.2358639 · Cited by 361
Weight Agnostic Neural Networks
Gaier, A., & Ha, D. (2019) · Unpublished · doi:10.13140/rg.2.2.16025.88169 · Cited by 161
Metadata
- Authors
- Konak, A., Coit, D. W., & Smith, A. E.
- Venue
- Reliability Engineering & System Safety
- Publisher
- Elsevier BV
- Published
- 2006-09
- Volume
- 91
- Issue
- 9
- Pages
- 992-1007
- ISSN
- 0951-8320 (ISSN-L)
- OpenAlex
- W1968535060
- Cited by
- 3,440
- References
- 42
- FWCI
- 33.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety. https://doi.org/10.1016/j.ress.2005.11.018
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
