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Título del libro: 2008 Ieee Congress On Evolutionary Computation, Cec 2008
Título del capítulo: A real-coded niching memetic algorithm for continuous multimodal function optimization

Autores UNAM:
JAVIER VITELA ESCAMILLA; OCTAVIO HECTOR CASTAÑOS GARZA;
Autores externos:
Idioma:
Año de publicación:
2008
Palabras clave:

Boolean functions; Evolutionary algorithms; Optimization; Probability density function; Sequential switching; Fitness landscapes; Highly sensitives; Local searches; Memetic algorithms; Multimodal function optimizations; Other algorithms; Performance measurements; Search spaces; Sequential niching techniques; Solution spaces; Standard test functions; Algorithms

Resumen:

In this work we extend the sequential niching technique of Beasley et at. for multiple optimal determination, incorporating a local search to improve accuracy. In the proposed method a sequence of GA runs make use of a derating function and of niching and clearing techniques to promote the occupation of different niches in the function to be optimized. The algorithm searches the solution space eliminating from the fitness landscape previously located peaks forcing the individuals to converge into unoccupied niches. Unlike other algorithms the efficiency of this sequential niching memetic algorithm (SNMA) is not highly sensitive to the niche radius. Performance measurements with standard test functions used by other researchers, show that the SNMA proposed outperforms other algorithms in accurately locating all optima, both global and local, in the search space. © 2008 IEEE.

Entidades citadas de la UNAM:
Fuente:



ISBN:
9781424418237
Editorial:

Tipo de producto:
Conference Paper



DOI:
10.1109/CEC.2008.4631087