A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization

  title={A Computationally Efficient Limited Memory CMA-ES for Large Scale Optimization},
  author={Ilya Loshchilov},
We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimization of non-linear, non-convex optimization problems in continuous domain. Inspired by the limited memory BFGS method of Liu and Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a covariance matrix reproduced from m direction vectors… CONTINUE READING
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