The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Hybrid methods using genetic algorithms for global optimization
Abstract
This paper discusses the trade-off between accuracy, reliability and computing time in global optimization. Particular compromises provided by traditional methods (Quasi-Newton and Nelder-Mead's simplex methods) and genetic algorithms are addressed and illustrated by a particular application in the field of nonlinear system identification. Subsequently, new hybrid methods are designed, combining principles from genetic algorithms and "hill-climbing" methods in order to find a better compromise to the trade-off. Inspired by biology and especially by the manner in which living beings adapt themselves to their environment, these hybrid methods involve two interwoven levels of optimization, namely evolution (genetic algorithms) and individual learning (Quasi-Newton), which cooperate in a global process of optimization. One of these hybrid methods appears to join the group of state-of-the-art global optimization methods: it combines the reliability properties of the genetic algorithms with the accuracy of Quasi-Newton method, while requiring a computation time only slightly higher than the latter.
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Cites
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Handbook of Genetic Algorithms
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Sheppard, P. M. (1971) · Journal of Medical Genetics · doi:10.1136/jmg.8.4.545-b · Cited by 1,627
System identification and control using genetic algorithms
Kristinsson, K., & Dumont, G. A. (1992) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/21.179842 · Cited by 517
An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms.
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Quasi-Newton Methods for Unconstrained Optimization
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Handling Constraints in Genetic Algorithms
Michalewicz, Z., & Janikow, C. Z. (1991) · international conference on Genetic algorithms · Cited by 326
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Cited by
Evolutionary computation: Comments on the history and current state
Back, T., Hammel, U., & Schwefel, H.-P. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585888 · Cited by 1,536
Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach
Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A., & Prasath, V. B. S. (2019) · Information · doi:10.3390/info10120390 · Cited by 668
Jacquemoud, S. (2000) · Remote Sensing of Environment · doi:10.1016/s0034-4257(00)00139-5 · Cited by 451
Metadata
- Authors
- Renders, J.-M., & Flasse, S. P.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 1996-04
- Volume
- 26
- Issue
- 2
- Pages
- 243-258
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2135038320
- Cited by
- 409
- References
- 16
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- closed
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- Renders, J.-M., & Flasse, S. P. (1996). Hybrid methods using genetic algorithms for global optimization. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics). https://doi.org/10.1109/3477.485836
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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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