Handling constraints in global optimization using artificial immune systems: A survey

Nareli Cruz-Cortés

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

12 Scopus citations

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.

Original languageEnglish
Title of host publicationConstraint-Handling in Evolutionary Optimization
EditorsEfren Mezura-Montes
PublisherSpringer Verlag
Pages237-262
Number of pages26
ISBN (Print)9783642006180
DOIs
StatePublished - 2009

Publication series

NameStudies in Computational Intelligence
Volume198
ISSN (Print)1860-949X

Keywords

  • Artificial immune systems
  • Constrained numerical optimization
  • Genetic algorithms

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