A parallel implementation of the covariance matrix adaptation evolution strategy
This work provides a faster optimization method for practitioners dealing with derivative-free problems, but it is incremental as it focuses on parallelization of an existing algorithm.
The authors tackled the slow execution time of the CMA-ES algorithm for derivative-free optimization by proposing a parallel implementation, achieving a 40% reduction in runtime on benchmark problems in the PythOPT environment.
In many practical optimization problems, the derivatives of the functions to be optimized are unavailable or unreliable. Such optimization problems are solved using derivative-free optimization techniques. One of the state-of-the-art techniques for derivative-free optimization is the covariance matrix adaptation evolution strategy (CMA-ES) algorithm. However, the complexity of CMA-ES algorithm makes it undesirable for tasks where fast optimization is needed. To reduce the execution time of CMA-ES, a parallel implementation is proposed, and its performance is analyzed using the benchmark problems in PythOPT optimization environment.