Deriving and improving CMA-ES with information geometric trust regions
CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm has many beneficial properties, including automatic step-size adaptation, efficient covariance updates that incorporates the current samp...
Main Author: | |
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Other Authors: | , , , |
Format: | conferencePaper |
Language: | eng |
Published: |
2017
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Subjects: | |
Online Access: | http://hdl.handle.net/1822/51451 |
Country: | Portugal |
Oai: | oai:repositorium.sdum.uminho.pt:1822/51451 |