Penalty Function Methods for Constrained Optimization with Genetic Algorithms
By Ozgur Yeniay. 2005
677 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/421c3443aae1f173ccd773cf53e27e984c0b7a10
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition (2020, methodology): “We employ the penalty method [51] and treat each probability constraint derived from the generic knowledge as a soft constraint.”
- A survey on evolutionary computation for complex continuous optimization (2021, methodology): “Firstly, the simplest penalty function method is to directly set the penalty factor as positive infinity, which is called “death penalty” (Yeniay 2005).”
- Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, background): “Although the use of penalty functions is very common since its simplicity and directapplicability(SmithandCoit1997;CoelloCoello1999; Yeniay 2005; Parsopoulos and Vrahatis 2002), they have several drawbacks, too.”