The DOI matches its Crossref record.
Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization
Abstract
In this paper a new genetic algorithm called the Breeder Genetic Algorithm (BGA) is introduced. The BGA is based on artificial selection similar to that used by human breeders. A predictive model for the BGA is presented that is derived from quantitative genetics. The model is used to predict the behavior of the BGA for simple test functions. Different mutation schemes are compared by computing the expected progress to the solution. The numerical performance of the BGA is demonstrated on a test suite of multimodal functions. The number of function evaluations needed to locate the optimum scales only as n ln(n) where n is the number of parameters. Results up to n = 1000 are reported.
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
Cited by
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569
Probabilistic Incremental Program Evolution
Salustowicz, R., & Schmidhuber, J. (1997) · Evolutionary Computation · doi:10.1162/evco.1997.5.2.123 · Cited by 249
Merrikh-Bayat, F. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.04.048 · Cited by 134
Is life impossible? Information, sex, and the origin of complex organisms.
Peck, J. R., & Waxman, D. (2010) · Evolution · doi:10.1111/j.1558-5646.2010.01074.x · Cited by 13
Metadata
- Authors
- Mühlenbein, H., & Schlierkamp-Voosen, D.
- Venue
- Evolutionary Computation
- Publisher
- MIT Press - Journals
- Published
- 1993-03
- Volume
- 1
- Issue
- 1
- Pages
- 25-49
- ISSN
- 1063-6560 (ISSN-L)
- 1530-9304
- OpenAlex
- W2027945080
- Cited by
- 1,199
- References
- 0
- FWCI
- 24.19
- Type
- journal article
- Language
- en
- Open access
- closed
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: Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization. https://app.cochat.ai/papers/10.1162/evco.1993.1.1.25
- The paper
- Mühlenbein, H., & Schlierkamp-Voosen, D. (1993). Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization. Evolutionary Computation. https://doi.org/10.1162/evco.1993.1.1.25
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.
