Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms

@article{Bosman2002MultiobjectiveOW,
  title={Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms},
  author={Peter A. N. Bosman and Dirk Thierens},
  journal={Int. J. Approx. Reasoning},
  year={2002},
  volume={31},
  pages={259-289}
}
Stochastic optimization by learning and using probabilistic models has received an increasing amount of attention over the last few years. Algorithms within this field estimate the probability distribution of a selection of the available solutions and subsequently draw more samples from the estimated probability distribution. The resulting algorithms have displayed a good performance on a wide variety of single– objective optimization problems, both for binary as well as for real–valued… CONTINUE READING
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