The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Fitness sharing and niching methods revisited
Open accessAbstract
Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, fitness sharing has been used widely to maintain population diversity and permit the investigation of manly peaks in the feasible domain. This paper reviews various strategies of sharing and proposes new recombination schemes to improve its efficiency. Some empirical results are presented for high and a limited number of fitness function evaluations. Finally, the study compares the sharing method with other niching techniques.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Uniform crossover in genetic algorithms
Syswerda, G. (1989) · international conference on Genetic algorithms · Cited by 1,965
Genetic algorithms with sharing for multimodal function optimization
Goldberg, D. E., & Richardson, J. T. (1987) · international conference on Genetic algorithms · Cited by 1,912
Reducing bias and inefficiency in the selection algorithm
Baker, J. E. (1987) · international conference on Genetic algorithms · Cited by 1,415
Niching methods for genetic algorithms
Mahfoud, S. W. (1996) · Cited by 897
An Investigation of Niche and Species Formation in Genetic Function Optimization
Deb, K., & Goldberg, D. E. (1989) · international conference on Genetic algorithms · Cited by 828
A clearing procedure as a niching method for genetic algorithms
Petrowski, A. (1996) · Proceedings of IEEE International Conference on Evolutionary Computation ICEC-96 · doi:10.1109/icec.1996.542703 · Cited by 630
A Sequential Niche Technique for Multimodal Function Optimization
Beasley, D., Bull, D., & Martin, R. R. (1993) · Evolutionary Computation · doi:10.1162/evco.1993.1.2.101 · Cited by 508
Cited by
Exploration and exploitation in evolutionary algorithms: A survey
Črepinšek, M., Liu, S.-H., & Mernik, M. (2013) · ACM Computing Surveys · doi:10.1145/2480741.2480752 · Cited by 1,298
Bio-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
Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology
Li, X. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2026270 · Cited by 466
Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications
Li, X., Epitropakis, M. G., Deb, K., & Engelbrecht, A. (2017) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2638437 · Cited by 339
Genetic algorithms for computational materials discovery accelerated by machine learning
Jennings, P. C., Lysgaard, S., Hummelshøj, J. S., Vegge, T., & Bligaard, T. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0181-4 · Cited by 269
Metadata
- Authors
- Sareni, B., & Krahenbuhl, L.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 1998
- Volume
- 2
- Issue
- 3
- Pages
- 97-106
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2142619464
- Cited by
- 578
- References
- 14
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fitness sharing and niching methods revisited. https://app.cochat.ai/papers/10.1109/4235.735432
- The paper
- Sareni, B., & Krahenbuhl, L. (1998). Fitness sharing and niching methods revisited. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/4235.735432
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.
