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Found 27 papers, 11 papers with code
Towards Physical Plausibility in Neuroevolution Systems
Even a slight reduction in power usage can lead to significant energy savings, benefiting users, companies, and the environment.
SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks
DENSER is a NE framework for the automatic design and parametrization of ANNs, based on the principles of Genetic Algorithms (GA) and Structured Grammatical Evolution (SGE).
Reducing the Price of Stable Cable Stayed Bridges with CMA-ES
There are two objectives when designing the bridges: minimizing the cost and maintaining the structural constraints in acceptable values to be considered safe.
Automatic Design of Telecom Networks with Genetic Algorithms
An AI-based solution is proposed to automate network design, which is a task typically done manually by teams of engineers.
All You Need Is Sex for Diversity
Although some mechanisms of Sexual Selection have been applied to Genetic Programming in the past, the literature is scarce when it comes to mate choice.
Context Matters: Adaptive Mutation for Grammars
This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation.
Structured mutation inspired by evolutionary theory enriches population performance and diversity
Grammar-Guided Genetic Programming (GGGP) employs a variety of insights from evolutionary theory to autonomously design solutions for a given task.
Adversarial Robustness Assessment of NeuroEvolution Approaches
Similarly to manually-designed networks, our results show that when the evolved models are attacked with iterative methods, their accuracy usually drops to, or close to, zero under both distance metrics.
Exploring Generative Adversarial Networks for Text-to-Image Generation with Evolution Strategies
In this work, we follow a different direction by proposing the use of Covariance Matrix Adaptation Evolution Strategy to explore the latent space of Generative Adversarial Networks.
Probabilistic Structured Grammatical Evolution
PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied.
Co-evolutionary Probabilistic Structured Grammatical Evolution
This work proposes an extension to Structured Grammatical Evolution (SGE) called Co-evolutionary Probabilistic Structured Grammatical Evolution (Co-PSGE).
On the Exploitation of Neuroevolutionary Information: Analyzing the Past for a More Efficient Future
Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures.
Evolving Learning Rate Optimizers for Deep Neural Networks
Learning Rate optimizers are a set of such techniques that search for good values of learning rates.
Probabilistic Grammatical Evolution
We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE).
Demonstrating the Evolution of GANs through t-SNE
Evolutionary algorithms, such as COEGAN, were recently proposed as a solution to improve the GAN training, overcoming common problems that affect the model, such as vanishing gradient and mode collapse.
Exploring the Evolution of GANs through Quality Diversity
We compare our proposal with the original COEGAN model and with an alternative version using a global competition approach.
AutoLR: An Evolutionary Approach to Learning Rate Policies
This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution.
Using Skill Rating as Fitness on the Evolution of GANs
Recent works proposed the use of evolutionary algorithms on GAN training, aiming to solve these challenges and to provide an automatic way to find good models.
Evolution of Scikit-Learn Pipelines with Dynamic Structured Grammatical Evolution
The deployment of Machine Learning (ML) models is a difficult and time-consuming job that comprises a series of sequential and correlated tasks that go from the data pre-processing, and the design and extraction of features, to the choice of the ML algorithm and its parameterisation.
Incremental Evolution and Development of Deep Artificial Neural Networks
Despite aiding non-expert users to design and train ANNs, the vast majority of NE approaches disregard the knowledge that is gathered when solving other tasks, i. e., evolution starts from scratch for each problem, ultimately delaying the evolutionary process.
COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks
COEGAN is a model that uses neuroevolution and coevolution in the GAN training algorithm to provide a more stable training method and the automatic design of neural network architectures.
Coevolution of Generative Adversarial Networks
Therefore, this project proposes COEGAN, a model that combines neuroevolution and coevolution in the coordination of the GAN training algorithm.
The Impact of Data Preparation on the Fairness of Software Systems
Data preparation is key in any machine learning pipeline, but its effect on fairness is yet to be studied in detail.
Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
The results show that the best CNN generated by Fast-DENSER++ improves by a factor of 2 when compared with the results reported by classic statistical approaches.
Fast-DENSER++: Evolving Fully-Trained Deep Artificial Neural Networks
This paper proposes a new extension to Deep Evolutionary Network Structured Evolution (DENSER), called Fast-DENSER++ (F-DENSER++).
DENSER: Deep Evolutionary Network Structured Representation
Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation.
Towards the Evolution of Multi-Layered Neural Networks: A Dynamic Structured Grammatical Evolution Approach
On the other, there is no way to evolve networks with more than one output neuron.
