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A local search template (extended abstract)

Rjm Rob Vaessens, Ehl Emile Aarts, JK Jan Karel Lenstra

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Abstract

A template is presented that captures the majority of local search algorithms proposed in the literature, such as iterative improvement, simulated annealing, threshold accepting, tabu search, and genetic algorithms. The template leads to a classification of existing local search algorithms and suggests directions for designing new types of local search approaches.\nKey words: local search, iterative improvement, simulated annealing, threshold accepting, tabu search, genetic algorithms.

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What this paper is about

A template is presented that captures the majority of local search algorithms proposed in the literature, such as iterative improvement, simulated annealing, threshold accepting, tabu search, and genetic algorithms. The template leads to a classification of existing local search algorithms and suggests directions for designing new types of local search approaches.\nKey words: local search, iterative improvement, simulated annealing, threshold accepting, tabu search, genetic algorithms.

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Available abstract

A template is presented that captures the majority of local search algorithms proposed in the literature, such as iterative improvement, simulated annealing, threshold accepting, tabu search, and genetic algorithms. The template leads to a classification of existing local search algorithms and suggests directions for designing new types of local search approaches.\nKey words: local search, iterative improvement, simulated annealing, threshold accepting, tabu search, genetic algorithms.

Key concepts: Tabu search, Guided Local Search, Hill climbing, Simulated annealing, Local search (optimization), Iterated local search, Beam search, Mathematical optimization

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