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On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's
Abstract
We discuss the use of non-stationary penalty functions to solve general nonlinear programming problems (NP) using real-valued GAs. The non-stationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas presented in this paper come from two basic areas: calculus-based nonlinear programming and simulated annealing. The non-stationary penalty methods are tested on four NP test cases and the effectiveness of these methods are reported.>
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Cited by
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Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization
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Push and pull search for solving constrained multi-objective optimization problems
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Metadata
- Authors
- Joines, J. A., & Houck, C. R.
- Venue
- Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence
- Publisher
- IEEE
- Published
- 2002-12-17
- Pages
- 579-584
- OpenAlex
- W2104324382
- Cited by
- 749
- References
- 0
- FWCI
- 30.45
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. https://app.cochat.ai/papers/10.1109/icec.1994.349995
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- Joines, J. A., & Houck, C. R. (2002). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. https://doi.org/10.1109/icec.1994.349995
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