An Asynchronous Implementation of the Limited Memory CMA-ES
This provides an incremental improvement for researchers and practitioners needing efficient parallel optimization of computationally expensive fitness functions.
The authors developed an asynchronous implementation of the LM-CMA-ES evolution strategy for large-scale continuous optimization, which outperforms the original version on benchmark functions like Rastrigin and achieves faster convergence on Rosenbrock and Ellipsoid functions.
We present our asynchronous implementation of the LM-CMA-ES algorithm, which is a modern evolution strategy for solving complex large-scale continuous optimization problems. Our implementation brings the best results when the number of cores is relatively high and the computational complexity of the fitness function is also high. The experiments with benchmark functions show that it is able to overcome its origin on the Sphere function, reaches certain thresholds faster on the Rosenbrock and Ellipsoid function, and surprisingly performs much better than the original version on the Rastrigin function.