Meta-Learning of Evolutionary Strategy for Stock Trading

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Journal of Data Analysis and Information Processing

ISSN Print: 2327-7211
ISSN Online: 2327-7203
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"Meta-Learning of Evolutionary Strategy for Stock Trading"
written by Erik Sorensen, Ryan Ozzello, Rachael Rogan, Ethan Baker, Nate Parks, Wei Hu,
published by Journal of Data Analysis and Information Processing, Vol.8 No.2, 2020

has been cited by the following article(s):

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CrossRef

[1]	Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning
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[2]	Application of evolutionary computation techniques in formulating short term tradings decisions in stock market
	Available at SSRN 4701751, 2024

[3]	Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem
	Applied Soft …, 2023

[4]	Adaptive Agents and Data Quality in Agent-Based Financial Markets
	Crain, BF Tivnan… - arXiv preprint arXiv …, 2023

[5]	Leveraging Domain Knowledge in Deep Learning Systems
	2021

[6]	Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey
	2021

[7]	Srovnání metod strojového učení využívaných v multiagentových systémech

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