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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey
verfasst von : Nareli Cruz-Cortés
Erschienen in: Constraint-Handling in Evolutionary Optimization
Verlag: Springer Berlin Heidelberg
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Artificial Immune Systems (AIS) are computational intelligent systems inspired by some processes or theories observed in the biological immune system. They have been applied to solve a wide range of machine learning and optimization problems. In this chapter the main AIS-based proposals for solving constrained numerical optimization problems are shown. Although the first works were hybrid solutions partially based on Genetic Algorithms, the most recent proposals are algorithms completely based on immune features.We show that these algorithms represent viable alternatives to the penalty functions and other similar mechanisms to handle constraints in numerical optimization problems.