Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Automated discovery of composite SAT variable-selection heuristics
Eighteenth national conference on Artificial intelligence
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Case-based heuristic selection for timetabling problems
Journal of Scheduling
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
A new heuristic algorithm for cuboids packing with no orientation constraints
Computers and Operations Research
Heuristic approaches for the two- and three-dimensional knapsack packing problem
Computers and Operations Research
A simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Evolving bin packing heuristics with genetic programming
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
VLSI module placement based on rectangle-packing by the sequence-pair
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
An indirect approach to the three-dimensional multi-pipe routing problem
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Matrix analysis of genetic programming mutation
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Data Structures for Higher-Dimensional Rectilinear Packing
INFORMS Journal on Computing
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This paper compares the quality of reusable heuristics designed by genetic programming (GP) to those designed by human programmers. The heuristics are designed for the three dimensional knapsack packing problem. Evolutionary computation has been employed many times to search for good quality solutions to such problems. However, actually designing heuristics with GP for this problem domain has never been investigated before. In contrast, the literature shows that it has taken years of experience by human analysts to design the very effective heuristic methods that currently exist. Hyper-heuristics search a space of heuristics, rather than directly searching a solution space. GP operates as a hyper-heuristic in this paper, because it searches the space of heuristics that can be constructed from a given set of components. We show that GP can design simple, yet effective, stand-alone constructive heuristics. While these heuristics do not represent the best in the literature, the fact that they are designed by evolutionary computation, and are human competitive, provides evidence that further improvements in this GP methodology could yield heuristics superior to those designed by humans.