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

Nareli Cruz-Cortés

Producción científica: Capítulo del libro/informe/acta de congresoCapítulorevisión exhaustiva

12 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaConstraint-Handling in Evolutionary Optimization
EditoresEfren Mezura-Montes
EditorialSpringer Verlag
Páginas237-262
Número de páginas26
ISBN (versión impresa)9783642006180
DOI
EstadoPublicada - 2009

Serie de la publicación

NombreStudies in Computational Intelligence
Volumen198
ISSN (versión impresa)1860-949X

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