Machine layout problem in flexible manufacturing systems
Operations Research
A new heuristic for the linear placement problem
Computers and Operations Research
Applications of cut polyhedra—I
Journal of Computational and Applied Mathematics
Applications of cut polyhedra—II
Journal of Computational and Applied Mathematics
Rank-Two Relaxation Heuristics for MAX-CUT and Other Binary Quadratic Programs
SIAM Journal on Optimization
A Spectral Bundle Method for Semidefinite Programming
SIAM Journal on Optimization
Distributed Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
MALLBA: A Library of Skeletons for Combinatorial Optimisation (Research Note)
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Parallel Metaheuristics: A New Class of Algorithms
Parallel Metaheuristics: A New Class of Algorithms
On greedy construction heuristics for the MAX-CUT problem
International Journal of Computational Science and Engineering
Detecting small group activities from multimodal observations
Applied Intelligence
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
Hybrid ensemble approach for classification
Applied Intelligence
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
A service oriented evolutionary architecture: applications and results
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Dealing with hardware heterogeneity: a new parallel search model
Natural Computing: an international journal
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This paper investigates a new heterogeneous method that dynamically sets the migration period of a distributed Genetic Algorithm (dGA). Each island GA of this multipopulation technique self-adapts the period for exchanging information with the other islands regarding the local evolution process. Thus, the different islands can develop different migration settings behaving like a heterogeneous dGA. The proposed algorithm is tested on a large set of instances of the Max-Cut problem, and it can be easily applied to other optimization problems. The results of this heterogeneous dGA are competitive with the best existing algorithms, with the added advantage of avoiding time-consuming preliminary tests for tuning the algorithm.