48 citations · 79 across the 3 of their papers we have counts for
8 papers
Speed Benchmarking of Genetic Programming Frameworks
Francisco Baeta, João Correia, Tiago Martins +1
Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design. While, over the years, many techniques have been developed to mitigate thi…
TensorGP -- Genetic Programming Engine in TensorFlow
Francisco Baeta, João Correia, Tiago Martins +1
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 Programm…
Demonstrating the Evolution of GANs through t-SNE
Victor Costa, Nuno Lourenço, João Correia +1
Generative Adversarial Networks (GANs) are powerful generative models that achieved strong results, mainly in the image domain. However, the training of GANs is not trivial, presen…
Exploring the Evolution of GANs through Quality Diversity
Victor Costa, Nuno Lourenço, João Correia +1
Generative adversarial networks (GANs) achieved relevant advances in the field of generative algorithms, presenting high-quality results mainly in the context of images. However, G…
Using Skill Rating as Fitness on the Evolution of GANs
Victor Costa, Nuno Lourenço, João Correia +1
Generative Adversarial Networks (GANs) are an adversarial model that achieved impressive results on generative tasks. In spite of the relevant results, GANs present some challenges…
COEGAN: Evaluating the Coevolution Effect in Generative Adversarial Networks
Victor Costa, Nuno Lourenço, João Correia +1
Generative adversarial networks (GAN) present state-of-the-art results in the generation of samples following the distribution of the input dataset. However, GANs are difficult to…