Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
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
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
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
Optimal job packing, a backfill scheduling optimization for a cluster of workstations
The Journal of Supercomputing
Automating the packing heuristic design process with genetic programming
Evolutionary Computation
A Flexible and Adaptive Hyper-heuristic Approach for (Dynamic) Capacitated Vehicle Routing Problems
Fundamenta Informaticae - Emergent Computing
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The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents two Evolutionary-Computation-based Models to producehyper-heuristics that solve two-dimensional bin-packing problems. The first model uses an XCS-type Learning Classifier System which learns a solution procedure when solving individual problems. The second model is based on a GA that uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through alearning process which includes training and testing phases.Both approaches, when tested and compared using a large set ofbenchmark problems, perform better than the combinations ofsingle heuristics. The testbed is composed of problems used inother similar studies in the literature. Some additional instances of the testbed were randomly generated.