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Evolving combinatorial problem instances that are difficult to solve

TitleEvolving combinatorial problem instances that are difficult to solve
Publication TypeJournal Article
Year of Publication2006
Authorsvan Hemert, JI
Journal TitleEvolutionary Computation
Volume14
Pages433--462
Type of Articlearticle
Keywordsconstraint programming; constraint satisfaction; evolutionary computation; problem evolving; satisfiability; travelling salesman
Abstract

In this paper we demonstrate how evolutionary computation can be used to acquire difficult to solve combinatorial problem instances, thereby stress-testing the corresponding algorithms used to solve these instances. The technique is applied in three important domains of combinatorial optimisation, binary constraint satisfaction, Boolean satisfiability, and the travelling salesman problem. Problem instances acquired through this technique are more difficult than ones found in popular benchmarks. We analyse these evolved instances with the aim to explain their difficulty in terms of structural properties, thereby exposing the weaknesses of corresponding algorithms.

URLhttp://www.mitpressjournals.org/toc/evco/14/4
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