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Evolving effective bidding functions for auction based resource allocation framework

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Standard

Evolving effective bidding functions for auction based resource allocation framework. / Bader, Mohamed; Fatima, S.

International conference on evolutionary computation, (ICEC 2009). ed. / A. Rosa. Madeira, Portugal : INSTICC, 2009. p. 310-313.

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Harvard

Bader, M & Fatima, S 2009, Evolving effective bidding functions for auction based resource allocation framework. in A Rosa (ed.), International conference on evolutionary computation, (ICEC 2009). INSTICC, Madeira, Portugal, pp. 310-313, International Conference on Evolutionary Computation (ICEC 2009), 1/10/09.

APA

Bader, M., & Fatima, S. (2009). Evolving effective bidding functions for auction based resource allocation framework. In A. Rosa (Ed.), International conference on evolutionary computation, (ICEC 2009) (pp. 310-313). Madeira, Portugal: INSTICC.

Vancouver

Bader M, Fatima S. Evolving effective bidding functions for auction based resource allocation framework. In Rosa A, editor, International conference on evolutionary computation, (ICEC 2009). Madeira, Portugal: INSTICC. 2009. p. 310-313

Author

Bader, Mohamed ; Fatima, S. / Evolving effective bidding functions for auction based resource allocation framework. International conference on evolutionary computation, (ICEC 2009). editor / A. Rosa. Madeira, Portugal : INSTICC, 2009. pp. 310-313

Bibtex

@inbook{5323b5d30e0a446a91d5b64bb53a592f,
title = "Evolving effective bidding functions for auction based resource allocation framework",
abstract = "In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling.",
keywords = "Genetic Programming, Auctions",
author = "Mohamed Bader and S. Fatima",
note = "Funders: EPSRC grant EP/G000980/1.",
year = "2009",
month = "10",
language = "English",
isbn = "9789896740146",
pages = "310--313",
editor = "A. Rosa",
booktitle = "International conference on evolutionary computation, (ICEC 2009)",
publisher = "INSTICC",

}

RIS

TY - CHAP

T1 - Evolving effective bidding functions for auction based resource allocation framework

AU - Bader, Mohamed

AU - Fatima, S.

N1 - Funders: EPSRC grant EP/G000980/1.

PY - 2009/10

Y1 - 2009/10

N2 - In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling.

AB - In this paper, we present an auction based resource allocation framework. This framework, called GPAuc, uses genetic programming for evolving bidding functions. We describe GPAuc in the context of the exam timetabling problem (ETTP). In the ETTP, there is a set of exams, which must be assigned to a predefined set of slots. Here, the exam time tabling system is the seller that auctions a set of slots. The exams are viewed as the bidding agents in need of slots. The problem is then to find a schedule (i.e., a slot for each exam) such that the total cost of conducting the exams as per the schedule is minimised. In order to arrive at such a schedule, we need to find the bidders' optimal bids. This is done using genetic programming. The effectiveness of GPAuc is demonstrated experimentally by comparing it with some existing benchmarks for exam time-tabling.

KW - Genetic Programming

KW - Auctions

M3 - Chapter (peer-reviewed)

SN - 9789896740146

SP - 310

EP - 313

BT - International conference on evolutionary computation, (ICEC 2009)

A2 - Rosa, A.

PB - INSTICC

CY - Madeira, Portugal

ER -

ID: 80007