A parallel genetic heuristic for the quadratic assignment problem
Proceedings of the third international conference on Genetic algorithms
Parallel genetic algorithms, population genetics and combinatorial optimization
Proceedings of the third international conference on Genetic algorithms
A genetic approach to the quadratic assignment problem
Computers and Operations Research - Special issue on genetic algorithms
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
Computational Optimization and Applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Nonlinear Assignment Problems: Algorithms and Applications (Combinatorial Optimization)
Solving large scale combinatorial optimization using PMA-SLS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Hybrid Metaheuristic for the Quadratic Assignment Problem
Computational Optimization and Applications
Adaptation for parallel memetic algorithm based on population entropy
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A GA-ACO-local search hybrid algorithm for solving quadratic assignment problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Memetic Algorithms for Feature Selection on Microarray Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Forma analysis of particle swarm optimisation for permutation problems
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
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A robust search algorithm should ideally exhibit reasonable performance on a diverse and varied set of problems. In an earlier paper Lim et al. (Computational Optimization and Applications, vol. 15, no. 3, 2000), we outlined a class of hybrid genetic algorithms based on the k-gene exchange local search for solving the quadratic assignment problem (QAP). We follow up on our development of the algorithms by reporting in this paper the results of comprehensive testing of the hybrid genetic algorithms (GA) in solving QAP. Over a hundred instances of QAP benchmarks were tested using a standard set of parameters setting and the results are presented along with the results obtained using simple GA for comparisons. Results of our testing on all the benchmarks show that the hybrid GA can obtain good quality solutions of within 2.5% above the best-known solution for 98% of the instances of QAP benchmarks tested. The computation time is also reasonable. For all the instances tested, all except for one require computation time not exceeding one hour. The results will serve as a useful baseline for performance comparison against other algorithms using the QAP benchmarks as a basis for testing.