The CMA Evolution Strategy: A Tutorial
2016-04-04Code Available2· sign in to hype
Nikolaus Hansen
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/nlinc1905/evolutionary-reinforcement-learnertf★ 0
- github.com/bionik-berlin/PURE_ESnone★ 0
- github.com/c-bata/goptunanone★ 0
- github.com/JohnYKiyo/coco_trialnone★ 0
- github.com/bgcarvalho/PINF-6073_natural-computingnone★ 0
- github.com/jyyang5/mu_mu_lambda-ESnone★ 0
- github.com/yn-cloud/CMAES.NETtf★ 0
- github.com/ppocma/ppocmatf★ 0
- github.com/ShangtongZhang/DistributedESpytorch★ 0
- github.com/WillButAgain/ENASpytorch★ 0
Abstract
This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.