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Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey

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Constraint-Handling in Evolutionary Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 198))

Abstract

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.

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Authors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute (CIC-IPN), 07738, Mexico City, Mexico

    Nareli Cruz-Cortés

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  1. Nareli Cruz-Cortés
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Editors and Affiliations

  1. National Laboratory on Advanced Informatics (LANIA A.C.) Rébsamen 80, CENTRO, 91000, Xalapa, Veracruz, Mexico

    Efrén Mezura-Montes

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Cruz-Cortés, N. (2009). Handling Constraints in Global Optimization Using Artificial Immune Systems: A Survey. In: Mezura-Montes, E. (eds) Constraint-Handling in Evolutionary Optimization. Studies in Computational Intelligence, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00619-7_11

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  • DOI: https://doi.org/10.1007/978-3-642-00619-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00618-0

  • Online ISBN: 978-3-642-00619-7

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