Optimering av hyperparametrar till artificiella neurala nätverk med genetiska algoritmer
(2016) FMS820 20161Mathematical Statistics
- Abstract
- This master thesis explores the feasibility of using genetic algorithms in order to automate the process of optimizing hyperparameters for artificial neural networks (ANN). Today there is no standard way to optimize hyperparameters for ANN; often they are set manually by trial and error. In order to explore the feasibility of using genetic algorithms to optimize hyperparameters for ANN, two algorithms are implemented in Python. The first is a genetic algorithm and the second is an algorithm that trains a neural network and enables predictions. The two algorithms interact in a feedback loop where the genetic algorithm adjusts hyperparameters for the neural network, and the neural network performs predictions on data which is used as... (More)
- This master thesis explores the feasibility of using genetic algorithms in order to automate the process of optimizing hyperparameters for artificial neural networks (ANN). Today there is no standard way to optimize hyperparameters for ANN; often they are set manually by trial and error. In order to explore the feasibility of using genetic algorithms to optimize hyperparameters for ANN, two algorithms are implemented in Python. The first is a genetic algorithm and the second is an algorithm that trains a neural network and enables predictions. The two algorithms interact in a feedback loop where the genetic algorithm adjusts hyperparameters for the neural network, and the neural network performs predictions on data which is used as feedback to the genetic algorithm. In order to evaluate the method, the implemented models are evaluated on three classification problems. The results are compared to predictions made from neural networks where the hyperparameters are manually set. The method using genetic algorithms to optimize hyperparameters performes slightly better on all three problems, but without a significant improvement in prediction accuracy. The implemented models offer an automated way to optimize hyperparameters for ANN and test results indicates that prediction accuracy is maintained. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8884763
- author
- Stensson, Simon
- supervisor
- organization
- course
- FMS820 20161
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- language
- Swedish
- id
- 8884763
- date added to LUP
- 2016-06-23 10:55:49
- date last changed
- 2016-06-23 10:55:49
@misc{8884763,
abstract = {{This master thesis explores the feasibility of using genetic algorithms in order to automate the process of optimizing hyperparameters for artificial neural networks (ANN). Today there is no standard way to optimize hyperparameters for ANN; often they are set manually by trial and error. In order to explore the feasibility of using genetic algorithms to optimize hyperparameters for ANN, two algorithms are implemented in Python. The first is a genetic algorithm and the second is an algorithm that trains a neural network and enables predictions. The two algorithms interact in a feedback loop where the genetic algorithm adjusts hyperparameters for the neural network, and the neural network performs predictions on data which is used as feedback to the genetic algorithm. In order to evaluate the method, the implemented models are evaluated on three classification problems. The results are compared to predictions made from neural networks where the hyperparameters are manually set. The method using genetic algorithms to optimize hyperparameters performes slightly better on all three problems, but without a significant improvement in prediction accuracy. The implemented models offer an automated way to optimize hyperparameters for ANN and test results indicates that prediction accuracy is maintained.}},
author = {{Stensson, Simon}},
language = {{swe}},
note = {{Student Paper}},
title = {{Optimering av hyperparametrar till artificiella neurala nätverk med genetiska algoritmer}},
year = {{2016}},
}