Authors:
Mohamed Bader-El-Den
and
Shaheen Fatima
Affiliation:
Loughborough University, United Kingdom
Keyword(s):
Genetic programming, Multi-agent systems, Resource allocation, Timetabling, Auctions based systems,
Heuristics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Game Theory Applications
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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.