A new heuristic algorithm solving the linear ordering problem
Computational Optimization and Applications
Intensification and diversification with elite tabu search solutions for the linear ordering problem
Computers and Operations Research
Fitness landscapes and memetic algorithm design
New ideas in optimization
Scatter search for the linear ordering problem
New ideas in optimization
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Stanford GraphBase: A Platform for Combinatorial Computing, The
Stanford GraphBase: A Platform for Combinatorial Computing, The
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Evolving feasible linear ordering problem solutions
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Differential Evolution and Genetic Algorithms for the Linear Ordering Problem
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
A genetic programming approach for solving the linear ordering problem
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
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The Linear Ordering Problem (LOP) is an NP-hard combinatorial optimization problem that arises in a variety of applications and several algorithmic approaches to its solution have been proposed. However, few details are known about the search space characteristics of LOP instances. In this article we develop a detailed study of the LOP search space. The results indicate that, in general, LOP instances show high fitness-distance correlations and large autocorrelation length but also that there exist significant differences between real-life and randomly generated LOP instances. Because of the limited size of real-world instances, we propose new, randomly generated large real-life like LOP instances which appear to be much harder than other randomly generated instances. Additionally, we propose a rather straightforward Iterated Local Search algorithm, which shows better performance than several state-of-the-art heuristics.