Calibrating strategies for evolutionary algorithms

Elizabeth Montero, María Cristina Riff

Resultado de la investigación: Conference contribution

2 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 originalEnglish
Título de la publicación alojada2007 IEEE Congress on Evolutionary Computation, CEC 2007
Páginas394-399
Número de páginas6
DOI
EstadoPublished - 1 dic 2007
Evento2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duración: 25 sep 200728 sep 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
PaísSingapore
Período25/09/0728/09/07

Huella dactilar

Evolutionary algorithms
Evolutionary Algorithms
Adaptive Techniques
Random Parameters
Reinforcement Learning
Control Parameter
Control Strategy
Tuning
Reinforcement learning
Operator
Strategy
Costs

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Citar esto

Montero, E., & Riff, M. C. (2007). Calibrating strategies for evolutionary algorithms. En 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 394-399). [4424498] https://doi.org/10.1109/CEC.2007.4424498
Montero, Elizabeth ; Riff, María Cristina. / Calibrating strategies for evolutionary algorithms. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. pp. 394-399
@inproceedings{47be3fab630146c697d3cf0659498e0b,
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",
month = "12",
day = "1",
doi = "10.1109/CEC.2007.4424498",
language = "English",
isbn = "1424413400",
pages = "394--399",
booktitle = "2007 IEEE Congress on Evolutionary Computation, CEC 2007",

}

Montero, E & Riff, MC 2007, Calibrating strategies for evolutionary algorithms. En 2007 IEEE Congress on Evolutionary Computation, CEC 2007., 4424498, pp. 394-399, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, Singapore, 25/09/07. https://doi.org/10.1109/CEC.2007.4424498

Calibrating strategies for evolutionary algorithms. / Montero, Elizabeth; Riff, María Cristina.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 394-399 4424498.

Resultado de la investigación: Conference contribution

TY - GEN

T1 - Calibrating strategies for evolutionary algorithms

AU - Montero, Elizabeth

AU - Riff, María Cristina

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=79955207157&partnerID=8YFLogxK

U2 - 10.1109/CEC.2007.4424498

DO - 10.1109/CEC.2007.4424498

M3 - Conference contribution

AN - SCOPUS:79955207157

SN - 1424413400

SN - 9781424413409

SP - 394

EP - 399

BT - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

ER -

Montero E, Riff MC. Calibrating strategies for evolutionary algorithms. En 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 394-399. 4424498 https://doi.org/10.1109/CEC.2007.4424498