A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
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
Scatter search for the linear ordering problem
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Path planning on a cuboid using genetic algorithms
Information Sciences: an International Journal
Evolving feasible linear ordering problem solutions
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Preference aggregation in group recommender systems for committee decision-making
Proceedings of the third ACM conference on Recommender systems
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
Revised GRASP with path-relinking for the linear ordering problem
Journal of Combinatorial Optimization
A benchmark library and a comparison of heuristic methods for the linear ordering problem
Computational Optimization and Applications
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), which is a well-known NP-hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure of designing a hybrid genetic algorithm, which includes the selection of crossover/mutation operators, accelerating the local search module and tuning the parameters. Experimental results show that our hybrid genetic algorithm outperforms all other existing exact and heuristic algorithms for this problem.