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Found 31 papers, 12 papers with code
Evaluation Metrics for Automated Typographic Poster Generation
Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task.
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.
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.
ESSYS* Sharing #UC: An Emotion-driven Audiovisual Installation
We present ESSYS* Sharing #UC, an audiovisual installation artwork that reflects upon the emotional context related to the university and the city of Coimbra, based on the data shared about them on Twitter.
Using Computational Approaches in Visual Identity Design: A Visual Identity for the Design and Multimedia Courses of Faculty of Sciences and Technology of University of Coimbra
In this work, we explore these computational approaches in order to generate a visual identity that creates bespoke letterings and images.
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.
Speed Benchmarking of Genetic Programming Frameworks
Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design.
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).
TensorGP -- Genetic Programming Engine in TensorFlow
In this paper, we resort to the TensorFlow framework to investigate the benefits of applying data vectorization and fitness caching methods to domain evaluation in Genetic Programming.
Performing Creativity With Computational Tools
The introduction of new tools in people's workflow has always been promotive of new creative paths.
Computers and Society
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.
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.
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.
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.
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++).
Evotype: Towards the Evolution of Type Stencils
Typefaces are an essential resource employed by graphic designers.
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.
A Pig, an Angel and a Cactus Walk Into a Blender: A Descriptive Approach to Visual Blending
A descriptive approach for automatic generation of visual blends is presented.
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.
