A Hyper-Heuristic Classifier for One Dimensional Bin Packing Problems: Improving Classification Accuracy by Attribute Evolution
Paechter, B. (2012). A Hyper-Heuristic Classifier for One Dimensional Bin Packing Problems: Improving Classification Accuracy by Attribute Evolution. In: (Ed.) Parallel Problem Solving from Nature: PPSN XII, Lecture Notes in Computer Science, 7492, () ( ed.). (pp. 348-357). Taormina: . Springer Verlag.
A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem instance. The EA evolves divisions of variable quantity and dimension that represent ranges of a bin’s capacity and are used to train a k-nearest neighbour algorithm. Once trained the classifier selects a single deterministic heuristic to solve
each one of a large set of unseen problem instances. The evolved classifier is shown to achieve results significantly better than are obtained by any of the constituent heuristics when used in isolation
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The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.