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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Volume were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2005, 10(1), 45-56; doi:10.3390/mca10010045

Penalty Function Methods for Constrained Optimization with Genetic Algorithms

Hacettepe University, Faculty of Science Department of Statistics, 06532, Beytepe, Ankara
Published: 1 April 2005
Download PDF [164 KB, uploaded 30 March 2016]


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
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Yeniay, Ö. Penalty Function Methods for Constrained Optimization with Genetic Algorithms. Math. Comput. Appl. 2005, 10, 45-56.

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