Large Scale Black-Box Optimization by Limited-Memory Matrix Adaptation
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Jul 11, 2018 · We present the limited-memory MA-ES for efficient zeroth order large-scale optimization. The algorithm demonstrates state-of-the-art performance on a set of ...
The Limited-Memory Matrix Adaptation Evolution Strategy (LM-MA-ES) is presented, demonstrating state-of-the-art performance on a set of established ...
The algorithm demonstrates state-of-the-art performance on a set of established large-scale benchmarks. I. INTRODUCTION. Evolution Strategies (ESs) are ...
May 22, 2017 · As such, LMMA-ES dramatically reduces the memory complexity of the original CMA-ES, hence resulting particularly suitable for solving large- ...
Nov 1, 2015 · The LM-CMA is a stochastic derivative-free algorithm for numerical optimization of non-linear, non-convex optimization problems.
Apr 1, 2019 · The limited-memory MA-ES is presented, a popular method to deal with nonconvex and/or stochastic optimization problems when gradient ...
We approximate the covariance matrix by a low-rank matrix with a few vectors and use two of them to generate each new solution. The algorithm achieves linear ...
Jul 19, 2022 · In this paper, we propose a distributed evolution strategy (DES) for large-scale black-box optimization (specifically with memory-costly function evaluations)
In this paper, we present a greatly improved version of the recently proposed ex- tension of CMA-ES to large scale optimization called the limited memory CMA-ES.
Aug 18, 2022 · In this paper, we present a pure-Python open-source library, called PyPop7, for black-box optimization (BBO). It provides a unified and modular ...