The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Constrained Optimization Via Genetic Algorithms
Abstract
This paper presents an application of genetic algorithms (GAs) to nonlinear constrained optimization. GAs are general purpose optimization algorithms which apply the rules of natural genetics to explore a given search space. When GAs are applied to nonlinear constrained problems, constraint handling becomes an important issue. The proposed search algorithm is realized by GAs which utilize a penalty function in the objective function to account for violation. This extension is based on systematic multi-stage assignments of weights in the penalty method as opposed to single-stage assignments in sequential unconstrained minimization. The experimental results are satisfactory and agree well with those of the gradient type methods.
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Cites
Genetic algorithms in search, optimization, and machine learning
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The Evolution of Strategies in the Iterated Prisoner's Dilemma
Axelrod, R. (2001) · Cited by 672
Cited by
Coello Coello, C. A. (2002) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(01)00323-1 · Cited by 2,343
Lee, K. S., & Geem, Z. W. (2005) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2004.09.007 · Cited by 1,772
Bat algorithm: a novel approach for global engineering optimization
Yang, X., & Hossein Gandomi, A. (2012) · Engineering Computations · doi:10.1108/02644401211235834 · Cited by 1,751
Use of a self-adaptive penalty approach for engineering optimization problems
Coello Coello, C. A. (2000) · Computers in Industry · doi:10.1016/s0166-3615(99)00046-9 · Cited by 1,316
Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization
Koziel, S., & Michalewicz, Z. (1999) · Evolutionary Computation · doi:10.1162/evco.1999.7.1.19 · Cited by 758
Metadata
- Authors
- Homaifar, A., Qi, C. X., & Lai, S. H.
- Venue
- SIMULATION
- Publisher
- SAGE Publications
- Published
- 1994-04
- Volume
- 62
- Issue
- 4
- Pages
- 242-253
- ISSN
- 0037-5497 (ISSN-L)
- 1741-3133
- OpenAlex
- W2093505886
- Cited by
- 802
- References
- 13
- FWCI
- 10.19
- 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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