Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Foundations of genetic programming
Foundations of genetic programming
The Phase-Transition Niche for Evolutionary Algorithms in Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Automated discovery of composite SAT variable-selection heuristics
Eighteenth national conference on Artificial intelligence
Genetic algorithms and timetabling
Advances in evolutionary computing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving rule induction algorithms with multi-objective grammar-based genetic programming
Knowledge and Information Systems
Linear linkage encoding in grouping problems: applications on graph coloring and timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Generating SAT local-search heuristics using a GP hyper-heuristic framework
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
A Field Guide to Genetic Programming
A Field Guide to Genetic Programming
Evolving bin packing heuristics with genetic programming
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Fuzzy multiple heuristic orderings for examination timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
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In this paper, we present a genetic programming (GP) framework for evolving agent's binding function (GPAuc) in a resource allocation problem. The framework is tested on the exam timetabling problem (ETP). There is a set of exams, which have to be assigned to a predefined set of slots and rooms. 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 timetabling.