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Implementations of Genetic Methods for Financial Machine Learning Applications

License: GNU General Public License v3.0

Python 0.22% Jupyter Notebook 44.34% HTML 55.44%
python genetic-algorithms genetic-programming evolutionary-computation topology-optimization hyperparameters-optimization symbolic-model financial-machine-learning

genetic-finance's Introduction

Application Cases

Case 1: Genetic Programming for Feature Engineering.

Repo used the DataDays 2021 talk -> Genetic Programming for Feature Engineering: An application in Trading System with Cryptocurrencies

Case 2: Genetic Algorithms for Hyperparameter Optimization.

Non-gradient-based optimization method, useful when having cost functions that are potentially non-diferentiable, discontinuous, with "cliffs" or "steps".

Case 3: Genetic Algorithms for Neural Network Topology Optimization.

To evolve the topology of a neural network in order to explore more variations of architectures. Insted of minization of a cost function, the goal is to maximize a fitness function.

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