Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems

@article{Tang2007DiversityadaptivePM,
  title={Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems},
  author={Jing Tang and Meng-Hiot Lim and Yew-Soon Ong},
  journal={Soft Comput.},
  year={2007},
  volume={11},
  pages={873-888}
}
Parallel Memetic Algorithms (PMAs) are a class of modern parallel meta-heuristics that combine evolutionary algorithms, local search, parallel and distributed computing technologies for global optimization. Recent studies on PMAs for large-scale complex combinatorial optimization problems have shown that they converge to high quality solutions significantly faster than canonical GAs and MAs. However, the use of local learning for every individual throughout the PMA search can be a very… CONTINUE READING
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Adaptive global optimization with local search

  • WE Hart
  • Ph. D. Thesis,
  • 1994
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