Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Developing a simulated annealing algorithm for the cutting stock problem
Computers and Industrial Engineering
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Hyper-heuristics: Learning To Combine Simple Heuristics In Bin-packing Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Examination Timetabling in British Universities: A Survey
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Hyperheuristic Approach to Scheduling a Sales Summit
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
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Case-based heuristic selection for timetabling problems
Journal of Scheduling
A new heuristic recursive algorithm for the strip rectangular packing problem
Computers and Operations Research
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem
Operations Research
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
Reactive GRASP for the strip-packing problem
Computers and Operations Research
The Bottomn-Left Bin-Packing Heuristic: An Efficient Implementation
IEEE Transactions on Computers
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
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 simple but theoretically-motivated method to control bloat in genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Generating SAT local-search heuristics using a GP hyper-heuristic framework
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Hill climbers and mutational heuristics in hyperheuristics
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Evolving bin packing heuristics with genetic programming
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Distributed choice function hyper-heuristics for timetabling and scheduling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Automating the packing heuristic design process with genetic programming
Evolutionary Computation
Learning heuristic policies – a reinforcement learning problem
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Matrix analysis of genetic programming mutation
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Hyper-heuristics with low level parameter adaptation
Evolutionary Computation
A developmental solution to (dynamic) capacitated arc routing problems using genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Generation of VNS components with grammatical evolution for vehicle routing
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A new hyper-heuristic as a general problem solver: an implementation in HyFlex
Journal of Scheduling
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
An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle
Applied Intelligence
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We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyperheuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain.