A practical solution to a fuzzy two-dimensional cutting stock problem
Fuzzy Sets and Systems
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Improved bounds for harmonic-based bin packing algorithms
Discrete Applied Mathematics - Special volume: combinatorics and theoretical computer science
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
On line bin packing with items of random size
Mathematics of Operations Research
BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem
Computers and Operations Research
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Exact Solution of the Two-Dimensional Finite Bon Packing Problem
Management Science
Best-fit bin-packing with random order
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
New Algorithms for Bin Packing
Journal of the ACM (JACM)
SIAM Journal on Discrete Mathematics
New Bounds for Variable-Sized Online Bin Packing
SIAM Journal on Computing
Paginating the Generalized Newspaper - A Comparison of Simulated Annealing and a Heuristic Method
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Automated discovery of composite SAT variable-selection heuristics
Eighteenth national conference on Artificial intelligence
A parallel tabu search algorithm for solving the container loading problem
Parallel Computing - Special issue: Parallel computing in logistics
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Proceedings of the 2005 ACM symposium on Applied computing
Hyper-heuristics and classifier systems for solving 2D-regular cutting stock problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Case-based heuristic selection for timetabling problems
Journal of Scheduling
A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
Operations Research
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new constraint programming approach for the orthogonal packing problem
Computers and Operations Research
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 Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem
INFORMS Journal on Computing
Arc-flow model for the two-dimensional guillotine cutting stock problem
Computers and Operations Research
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics
IEEE Transactions on Evolutionary Computation
Evolving bin packing heuristics with genetic programming
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Building hyper-heuristics through ant colony optimization for the 2d bin packing problem
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Generation of VNS components with grammatical evolution for vehicle routing
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
HH-evolver: a system for domain-specific, hyper-heuristic evolution
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Genetic programming for evolving due-date assignment models in job shop environments
Evolutionary Computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.