Abstract
Constraint handling is one of the major concerns when applying genetic algorithms (GAs) to solve constrained optimization problems. This paper proposes to use the gradient information derived from the constraint set to systematically repair infeasible solutions. The proposed repair procedure is embedded into a simple GA as a special operator. Experiments using 11 benchmark problems are presented and compared with the best known solutions reported in the literature. Our results are competitive, if not better, compared to the results reported using the homomorphous mapping method, the stochastic ranking method, and the self-adaptive fitness formulation method.
Original language | English |
---|---|
Pages (from-to) | 2263-2281 |
Number of pages | 19 |
Journal | Computers and Operations Research |
Volume | 33 |
Issue number | 8 |
DOIs | |
Publication status | Published - 1 Aug 2006 |
Externally published | Yes |
Keywords
- Constrained optimization
- Constraint handling
- Genetic algorithms
- Hybrid method
ASJC Scopus subject areas
- Information Systems and Management
- Management Science and Operations Research
- Applied Mathematics
- Modelling and Simulation
- Transportation