Cover Image

A comparative study for parameter selection in online auctions

In this information-rich age, online auctions have become an important procurement tool in either commercial or personal use. As the number of auctions increases, the process of monitoring, tracking bid and bidding in multiple auctions become a problem. The user needs to monitor many auctions sites,...

Full description

Saved in:
Bibliographic Details
Main Author: Gan, Kim Soon
Format: Thesis
Language:English
English
Published: 2009
Subjects:
HF5469.7-5481 Markets. Fairs
Online Access:https://eprints.ums.edu.my/id/eprint/42523/2/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/42523/1/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/42523/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ums.eprints.42523
record_format eprints
spelling my.ums.eprints.425232025-01-07T02:53:18Z https://eprints.ums.edu.my/id/eprint/42523/ A comparative study for parameter selection in online auctions Gan, Kim Soon HF5469.7-5481 Markets. Fairs In this information-rich age, online auctions have become an important procurement tool in either commercial or personal use. As the number of auctions increases, the process of monitoring, tracking bid and bidding in multiple auctions become a problem. The user needs to monitor many auctions sites, picks the right auction to participate, and makes the right bid in making sure that the desired item satisfies the user's preference. All these tasks are somewhat complex and time consuming. The task even gets more complicated when there are different start and end times and when the auctions employ different protocols. Due to the complex and dynamic nature of the online auction, one of the strategies employed is using genetic algorithm to discover the best strategy. Hence, this work attempts to improve an existing bidding strategy by taking into accounts the evolution of various model of generic algorithm in optimizing the parameter of the bidding strategies. In this work, three different models of genetic algorithms are considered. In the first model, the crossover and the mutation rate of the genetic algorithms are varied in order to create different combination of crossover and mutation rate. The new combination of genetic probabilities from this investigation will eventually perform better than the recommended genetic probabilities adopted in the previous work. The second model is the dynamic adaptation model namely the dynamic deterministic adaptive model. The bidding strategy from the experimental result of this experiment will eventually perform better than the bidding strategy that applied fixed static genetic operator's probabilities. Self adaptation genetic algorithm is the last model that will be used to evolve the bidding strategy. The bidding strategies applying self-adaptation model are expected to perform better than the deterministic dynamic adaptation because of the nature of the algorithm itself. The evaluations are conducted in a simulated online auction framework with multiple auctions running concurrently. The effectiveness of the bidding strategies is measured based on the average fitness of the individuals, the success rate and average payoff in obtaining the item in the auctions. The performance of these bidding strategies will be empirically demonstrated in this thesis. 2009 Thesis NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/42523/2/24%20PAGES.pdf text en https://eprints.ums.edu.my/id/eprint/42523/1/FULLTEXT.pdf Gan, Kim Soon (2009) A comparative study for parameter selection in online auctions. Masters thesis, Univerisiti Malaysia Sabah.
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic HF5469.7-5481 Markets. Fairs
spellingShingle HF5469.7-5481 Markets. Fairs
Gan, Kim Soon
A comparative study for parameter selection in online auctions
description In this information-rich age, online auctions have become an important procurement tool in either commercial or personal use. As the number of auctions increases, the process of monitoring, tracking bid and bidding in multiple auctions become a problem. The user needs to monitor many auctions sites, picks the right auction to participate, and makes the right bid in making sure that the desired item satisfies the user's preference. All these tasks are somewhat complex and time consuming. The task even gets more complicated when there are different start and end times and when the auctions employ different protocols. Due to the complex and dynamic nature of the online auction, one of the strategies employed is using genetic algorithm to discover the best strategy. Hence, this work attempts to improve an existing bidding strategy by taking into accounts the evolution of various model of generic algorithm in optimizing the parameter of the bidding strategies. In this work, three different models of genetic algorithms are considered. In the first model, the crossover and the mutation rate of the genetic algorithms are varied in order to create different combination of crossover and mutation rate. The new combination of genetic probabilities from this investigation will eventually perform better than the recommended genetic probabilities adopted in the previous work. The second model is the dynamic adaptation model namely the dynamic deterministic adaptive model. The bidding strategy from the experimental result of this experiment will eventually perform better than the bidding strategy that applied fixed static genetic operator's probabilities. Self adaptation genetic algorithm is the last model that will be used to evolve the bidding strategy. The bidding strategies applying self-adaptation model are expected to perform better than the deterministic dynamic adaptation because of the nature of the algorithm itself. The evaluations are conducted in a simulated online auction framework with multiple auctions running concurrently. The effectiveness of the bidding strategies is measured based on the average fitness of the individuals, the success rate and average payoff in obtaining the item in the auctions. The performance of these bidding strategies will be empirically demonstrated in this thesis.
format Thesis
author Gan, Kim Soon
author_facet Gan, Kim Soon
author_sort Gan, Kim Soon
title A comparative study for parameter selection in online auctions
title_short A comparative study for parameter selection in online auctions
title_full A comparative study for parameter selection in online auctions
title_fullStr A comparative study for parameter selection in online auctions
title_full_unstemmed A comparative study for parameter selection in online auctions
title_sort comparative study for parameter selection in online auctions
publishDate 2009
url https://eprints.ums.edu.my/id/eprint/42523/2/24%20PAGES.pdf
https://eprints.ums.edu.my/id/eprint/42523/1/FULLTEXT.pdf
https://eprints.ums.edu.my/id/eprint/42523/
_version_ 1821003228472410112
score 13.23648

Similar Items