Genetic algorithm for constrained global optimization in continuous variables
We present a stochastic global optimization algorithm, referred to as a Genetic Algorithm (GA), for solving constrained optimization problems over a compact search domain. It is a real-coded GA that converges in probability to the optimal solution. The constraints are treated through a repair operat...
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| Main Authors: | , |
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| Format: | Journal |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=28544431906&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/62287 |
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| Institution: | Chiang Mai University |
| Summary: | We present a stochastic global optimization algorithm, referred to as a Genetic Algorithm (GA), for solving constrained optimization problems over a compact search domain. It is a real-coded GA that converges in probability to the optimal solution. The constraints are treated through a repair operator. A specific repair operator is included for linear inequality constraints. © 2005 Elsevier Inc. All rights reserved. |
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