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

Engineering School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages	394-399
Number of pages	6
DOIs	https://doi.org/10.1109/CEC.2007.4424498
Publication status	Published - 1 Dec 2007
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Country/Territory	Singapore
Period	25/09/07 → 28/09/07

ASJC Scopus subject areas

Artificial Intelligence
Software
Theoretical Computer Science

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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

SP - 394

EP - 399

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

Abstract

Conference

ASJC Scopus subject areas

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