The memory indexing evolutionary algorithm for dynamic environments

Aydin Karaman; Şima Uyar; Gülşen Eryiǧit

doi:10.1007/978-3-540-32003-6_59

The memory indexing evolutionary algorithm for dynamic environments

Aydin Karaman^*, Şima Uyar, Gülşen Eryiǧit

^*Corresponding author for this work

Department of Artificial Intelligence and Data Engineering

Istanbul Technical University

Research output: Contribution to journal › Conference article › peer-review

20 Citations (Scopus)

Abstract

There is a growing interest in applying evolutionary algorithms to dynamic environments. Different types of changes in the environment benefit from different types of mechanisms to handle the change. In this study, the mechanisms used in literature are categorized into four groups. A new EA approach (MIA) which benefits from the EDA-like approach it employs for re-initializing populations after a change as well as using different change handling mechanisms together is proposed. Experiments are conducted using the 0/1 single knapsack problem to compare MIA with other algorithms and to explore its performance. Promising results are obtained which promote further study. Current research is being done to extend MIA to other problem domains.

Original language	English
Pages (from-to)	563-573
Number of pages	11
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3449
DOIs	https://doi.org/10.1007/978-3-540-32003-6_59
Publication status	Published - 2005
Event	EvoWorkshops 2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, and EvoSTOC - Lausanne, Switzerland Duration: 30 Mar 2005 → 1 Apr 2005

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title = "The memory indexing evolutionary algorithm for dynamic environments",

abstract = "There is a growing interest in applying evolutionary algorithms to dynamic environments. Different types of changes in the environment benefit from different types of mechanisms to handle the change. In this study, the mechanisms used in literature are categorized into four groups. A new EA approach (MIA) which benefits from the EDA-like approach it employs for re-initializing populations after a change as well as using different change handling mechanisms together is proposed. Experiments are conducted using the 0/1 single knapsack problem to compare MIA with other algorithms and to explore its performance. Promising results are obtained which promote further study. Current research is being done to extend MIA to other problem domains.",

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AU - Uyar, Şima

AU - Eryiǧit, Gülşen

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N2 - There is a growing interest in applying evolutionary algorithms to dynamic environments. Different types of changes in the environment benefit from different types of mechanisms to handle the change. In this study, the mechanisms used in literature are categorized into four groups. A new EA approach (MIA) which benefits from the EDA-like approach it employs for re-initializing populations after a change as well as using different change handling mechanisms together is proposed. Experiments are conducted using the 0/1 single knapsack problem to compare MIA with other algorithms and to explore its performance. Promising results are obtained which promote further study. Current research is being done to extend MIA to other problem domains.

AB - There is a growing interest in applying evolutionary algorithms to dynamic environments. Different types of changes in the environment benefit from different types of mechanisms to handle the change. In this study, the mechanisms used in literature are categorized into four groups. A new EA approach (MIA) which benefits from the EDA-like approach it employs for re-initializing populations after a change as well as using different change handling mechanisms together is proposed. Experiments are conducted using the 0/1 single knapsack problem to compare MIA with other algorithms and to explore its performance. Promising results are obtained which promote further study. Current research is being done to extend MIA to other problem domains.

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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The memory indexing evolutionary algorithm for dynamic environments

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