Evolving Differentiable Gene Regulatory Networks

07/16/2018
by   Dennis G Wilson, et al.
0

Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have shown their capacity to solve real-world problems in various domains such as agent control, signal processing and artificial life experiments. They have also benefited from new evolutionary approaches and improvements to dynamic which have increased their optimization efficiency. In this paper, we present an additional step toward their usability in machine learning applications. We detail an GPU-based implementation of differentiable GRNs, allowing for local optimization of GRN architectures with stochastic gradient descent (SGD). Using a standard machine learning dataset, we evaluate the ways in which evolution and SGD can be combined to further GRN optimization. We compare these approaches with neural network models trained by SGD and with support vector machines.

READ FULL TEXT
research
05/13/2014

Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling

Stochastic Gradient Descent (SGD) is a popular optimization method which...
research
10/16/2018

Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks

We propose a population-based Evolutionary Stochastic Gradient Descent (...
research
05/20/2023

Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning

Whenever applicable, the Stochastic Gradient Descent (SGD) has shown its...
research
02/24/2018

Stochastic Gradient Descent on Highly-Parallel Architectures

There is an increased interest in building data analytics frameworks wit...
research
01/10/2020

Choosing the Sample with Lowest Loss makes SGD Robust

The presence of outliers can potentially significantly skew the paramete...
research
12/18/2017

On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent

Because stochastic gradient descent (SGD) has shown promise optimizing n...

Please sign up or login with your details

Forgot password? Click here to reset