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Article

Penalty Function Methods for Constrained Optimization with Genetic Algorithms

Hacettepe University, Faculty of Science Department of Statistics, 06532, Beytepe, Ankara
Math. Comput. Appl. 2005, 10(1), 45-56; https://doi.org/10.3390/mca10010045
Published: 1 April 2005
Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to handle constraints is to use penalty functions. In this paper, we present these penalty-based methods and discuss their strengths and weaknesses.
Keywords: Genetic algorithms; Optimization, Constraint handling; Penalty function Genetic algorithms; Optimization, Constraint handling; Penalty function
MDPI and ACS Style

Yeniay, Ö. Penalty Function Methods for Constrained Optimization with Genetic Algorithms. Math. Comput. Appl. 2005, 10, 45-56. https://doi.org/10.3390/mca10010045

AMA Style

Yeniay Ö. Penalty Function Methods for Constrained Optimization with Genetic Algorithms. Mathematical and Computational Applications. 2005; 10(1):45-56. https://doi.org/10.3390/mca10010045

Chicago/Turabian Style

Yeniay, Özgür. 2005. "Penalty Function Methods for Constrained Optimization with Genetic Algorithms" Mathematical and Computational Applications 10, no. 1: 45-56. https://doi.org/10.3390/mca10010045

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