Adaptive global optimization with local search
Adaptive global optimization with local search
Computational Optimization and Applications
QAPLIB – A Quadratic Assignment ProblemLibrary
Journal of Global Optimization
Extensive Testing of a Hybrid Genetic Algorithm for Solving Quadratic Assignment Problems
Computational Optimization and Applications
Evolutionary algorithms with local search for combinatorial optimization
Evolutionary algorithms with local search for combinatorial optimization
Solving large scale combinatorial optimization using PMA-SLS
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Applying memetic algorithms to the analysis of microarray data
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
A multi-cluster grid enabled evolution framework for aerodynamic airfoil design optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
A study of the Lamarckian evolution of recurrent neural networks
IEEE Transactions on Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Systematic integration of parameterized local search into evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we propose the island model parallel memetic algorithm with diversity-based dynamic adaptive strategy (PMA-DLS) for controlling the local search frequency and demonstrate its utility in solving complex combinatorial optimization problems, in particular large-scale quadratic assignment problems (QAPs). The empirical results show that PMA-DLS converges to competitive solutions at significantly lower computational cost when compared to the canonical MA and PMA. Furthermore, compared to our previous work on PMA using static adaptation strategy, it is found that the diversity-based dynamic adaptation strategy displays better robustness in terms of solution quality across the class of QAP problems considered without requiring extra effort in selecting suitable parameters.