Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
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
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Simultaneously Applying Multiple Mutation Operators in Genetic Algorithms
Journal of Heuristics
An Experimental Evaluation of a Scatter Search for the Linear Ordering Problem
Journal of Global Optimization
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Variable neighborhood search for the linear ordering problem
Computers and Operations Research
Solving the linear ordering problem using ant models
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A Hybrid Ant-Based Approach to the Economic Triangulation Problem for Input-Output Tables
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Designing a hybrid genetic algorithm for the linear ordering problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Search space analysis of the linear ordering problem
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization
The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization
A benchmark library and a comparison of heuristic methods for the linear ordering problem
Computational Optimization and Applications
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The linear ordering problem (LOP) consists in rearranging the rows and columns of a given square matrix such that the sum of the super-diagonal entries is as large as possible. The LOP has a significant number of important practical applications. In this paper we describe an efficient genetic programming based algorithm, designed to find high quality solutions for LOP. The computational results obtained for two sets of benchmark instances indicate that our proposed heuristic is competitive to previous methods for solving the LOP.