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https://doi.org/10.1109/cec.20...
Article . 2005 . Peer-reviewed
Data sources: Crossref
DBLP
Conference object . 2019
Data sources: DBLP
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Analysis of Ausubel Auctions by Means of Evolutionary Computation

Authors: Asunción Mochón; David Quintana; Yago Sáez; Pedro Isasi Viñuela;

Analysis of Ausubel Auctions by Means of Evolutionary Computation

Abstract

IEEE Congress on Evolutionary Computation. Edimburgo, 2-5 September 2005 The increasing use of auctions has led to a growing interest in the subject. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this paper is to develop a genetic algorithm to find bidders' optimal strategies for a specific dynamic multi-unit auction. The algorithm provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform canonical strategies.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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3
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34
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