A Spectral Bundle Method for Semidefinite Programming
SIAM Journal on Optimization
Fitness landscapes and evolvability
Evolutionary Computation
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
MALLBA: A Library of Skeletons for Combinatorial Optimisation (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
On greedy construction heuristics for the MAX-CUT problem
International Journal of Computational Science and Engineering
Advanced Scatter Search for the Max-Cut Problem
INFORMS Journal on Computing
Competitive simulated annealing and Tabu Search algorithms for the max-cut problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Dealing with hardware heterogeneity: a new parallel search model
Natural Computing: an international journal
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Tuning distributed genetic algorithms (dGAs) increases even more the task of finding an appropriate parameterization, since the migration operator adds, at least, five additional values that have to be set up. This work is a preliminary approach on using a landscape measure (the Fitness Distance Correlation) to dynamically adjust one of these five parameters, in particular, the migration period. The results have shown that, by using this information, the quality of the solutions is competitive with those obtained by the algorithms with the pre-tuned migration period, but with a saving of more than 100 hours of preliminary experimentation.