Calibrating strategies for evolutionary algorithms

Elizabeth Montero; María Cristina Riff

doi:10.1109/CEC.2007.4424498

Calibrating strategies for evolutionary algorithms

Elizabeth Montero, María Cristina Riff

Facultad Ingeniería

Producción científica: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

4 Citas (Scopus)

Resumen

The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control.

Idioma original	Inglés
Título de la publicación alojada	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Páginas	394-399
Número de páginas	6
DOI	https://doi.org/10.1109/CEC.2007.4424498
Estado	Publicada - 1 dic. 2007
Evento	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapur Duración: 25 sep. 2007 → 28 sep. 2007

Conferencia

Conferencia	2007 IEEE Congress on Evolutionary Computation, CEC 2007
País/Territorio	Singapur
Período	25/09/07 → 28/09/07

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Software
Ciencia computacional teórica

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10.1109/CEC.2007.4424498

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title = "Calibrating strategies for evolutionary algorithms",

abstract = "The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control.",

author = "Elizabeth Montero and Riff, {Mar{\'i}a Cristina}",

year = "2007",

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AU - Riff, María Cristina

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AB - The control of parameters during the execution of evolutionary algorithms is an open research area. In this paper, we propose new parameter control strategies for evolutionary approaches, based on reinforcement learning ideas. Our approach provides efficient and low cost adaptive techniques for parameter control. Moreover, it is a general method, thus it could be applied to any evolutionary approach having more than one operator. We contrast our results with tuning techniques and HAEA a random parameter control.

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U2 - 10.1109/CEC.2007.4424498

DO - 10.1109/CEC.2007.4424498

M3 - Conference contribution

AN - SCOPUS:79955207157

SN - 1424413400

SN - 9781424413409

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BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Calibrating strategies for evolutionary algorithms

Resumen

Conferencia

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