Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles

  title={Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles},
  author={Yann Ollivier and Ludovic Arnold and Anne Auger and Nikolaus Hansen},
  journal={Journal of Machine Learning Research},
We present a canonical way to turn any smooth parametric family of probability distributions on an arbitrary search space X into a continuous-time black-box optimization method on X , the informationgeometric optimization (IGO) method. Invariance as a major design principle keeps the number of arbitrary choices to a minimum. The resulting method conducts a natural gradient ascent using an adaptive, time-dependent transformation of the objective function, and makes no particular assumptions on… CONTINUE READING
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