Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th 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
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
Improving Cutting-Stock Plans with Multi-objective Genetic Algorithms
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
A multi-objective hyper-heuristic based on choice function
Expert Systems with Applications: An International Journal
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This article presents a method based on the multi-objective evolutionary algorithm NSGA-II to approximate hyper-heuristics for solving irregular 2D cutting stock problems under multiple objectives. In this case, additionally to the traditional objective of minimizing the number of sheets used to fit a finite number of irregular pieces, the time required to perform the placement task is also minimized, leading to a bi-objective minimization problem with a tradeoff between the number of sheets and the time required for placing all pieces. We solve this problem using multi-objective hyper-heuristics (MOHHs), whose main idea consists of finding a set of simple heuristics which can be combined to find a general solution for a wide range of problems, where a single heuristic is applied depending on the current condition of the problem, instead of applying a unique single heuristic during the whole placement process. The MOHHs are approximated after going through a learning process by mean of the NSGA-II, which evolves combinations of condition-action rules producing at the end a set of Pareto-optimal MOHHs.We tested the approximated MMOHHs on several sets of benchmark problems, having outstanding results for most of the cases.