1 code implementation • 10 Feb 2024 • Sérgio M. Rebelo, J. J. Merelo, João Bicker, Penousal Machado
Computational Design approaches facilitate the generation of typographic design, but evaluating these designs remains a challenging task.
no code implementations • 31 Jan 2024 • Gabriel Cortês, Nuno Lourenço, Penousal Machado
Even a slight reduction in power usage can lead to significant energy savings, benefiting users, companies, and the environment.
no code implementations • 30 Mar 2023 • José Maria Simões, Nuno Lourenço, Penousal Machado
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.
1 code implementation • 25 Mar 2023 • Pedro Carvalho, Jessica Mégane, Nuno Lourenço, Penousal Machado
This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated variation.
no code implementations • 1 Feb 2023 • Stefano Tiso, Pedro Carvalho, Nuno Lourenço, Penousal Machado
Grammar-Guided Genetic Programming (GGGP) employs a variety of insights from evolutionary theory to autonomously design solutions for a given task.
no code implementations • 7 Sep 2022 • Sérgio M. Rebelo, Mariana Seiça, Pedro Martins, João Bicker, Penousal Machado
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.
no code implementations • 7 Sep 2022 • Sérgio M. Rebelo, Tiago Martins, Artur Rebelo, João Bicker, Penousal Machado
In this work, we explore these computational approaches in order to generate a visual identity that creates bespoke letterings and images.
1 code implementation • 6 Jul 2022 • Victor Costa, Nuno Lourenço, João Correia, Penousal Machado
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.
1 code implementation • 21 May 2022 • Jessica Mégane, Nuno Lourenço, Penousal Machado
PSGE statistically outperformed Grammatical Evolution (GE) on all six benchmark problems studied.
1 code implementation • 19 Apr 2022 • Jessica Mégane, Nuno Lourenço, Penousal Machado
This work proposes an extension to Structured Grammatical Evolution (SGE) called Co-evolutionary Probabilistic Structured Grammatical Evolution (Co-PSGE).
no code implementations • 26 May 2021 • Unai Garciarena, Nuno Lourenço, Penousal Machado, Roberto Santana, Alexander Mendiburu
Neuroevolutionary algorithms, automatic searches of neural network structures by means of evolutionary techniques, are computationally costly procedures.
no code implementations • 25 May 2021 • Francisco Baeta, João Correia, Tiago Martins, Penousal Machado
Genetic Programming (GP) is known to suffer from the burden of being computationally expensive by design.
no code implementations • 23 Mar 2021 • Pedro Carvalho, Nuno Lourenço, Penousal Machado
Learning Rate optimizers are a set of such techniques that search for good values of learning rates.
1 code implementation • 15 Mar 2021 • Jessica Mégane, Nuno Lourenço, Penousal Machado
We evaluate the performance of PGE in two regression problems and compare it with GE and Structured Grammatical Evolution (SGE).
1 code implementation • 12 Mar 2021 • Francisco Baeta, João Correia, Tiago Martins, Penousal Machado
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.
no code implementations • 9 Mar 2021 • Daniel Lopes, Jéssica Parente, Pedro Silva, Licínio Roque, Penousal Machado
The introduction of new tools in people's workflow has always been promotive of new creative paths.
Computers and Society
no code implementations • 31 Jan 2021 • Victor Costa, Nuno Lourenço, João Correia, Penousal Machado
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.
1 code implementation • 13 Jul 2020 • Victor Costa, Nuno Lourenço, João Correia, Penousal Machado
We compare our proposal with the original COEGAN model and with an alternative version using a global competition approach.
no code implementations • 8 Jul 2020 • Pedro Carvalho, Nuno Lourenço, Filipe Assunção, Penousal Machado
This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution.
no code implementations • 9 Apr 2020 • Victor Costa, Nuno Lourenço, João Correia, Penousal Machado
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.
1 code implementation • 1 Apr 2020 • Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado
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.
1 code implementation • 1 Apr 2020 • Filipe Assunção, Nuno Lourenço, Bernardete Ribeiro, Penousal Machado
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.
1 code implementation • 12 Dec 2019 • Victor Costa, Nuno Lourenço, João Correia, Penousal Machado
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.
no code implementations • 12 Dec 2019 • Victor Costa, Nuno Lourenço, Penousal Machado
Therefore, this project proposes COEGAN, a model that combines neuroevolution and coevolution in the coordination of the GAN training algorithm.
no code implementations • 9 May 2019 • Filipe Assunção, João Correia, Rúben Conceição, Mário Pimenta, Bernardo Tomé, Nuno Lourenço, Penousal Machado
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.
no code implementations • 8 May 2019 • Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
This paper proposes a new extension to Deep Evolutionary Network Structured Evolution (DENSER), called Fast-DENSER++ (F-DENSER++).
no code implementations • 26 Jun 2018 • Tiago Martins, João Correia, Ernesto Costa, Penousal Machado
Typefaces are an essential resource employed by graphic designers.
17 code implementations • 4 Jan 2018 • Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
Deep Evolutionary Network Structured Representation (DENSER) is a novel approach to automatically design Artificial Neural Networks (ANNs) using Evolutionary Computation.
no code implementations • 27 Jun 2017 • João M. Cunha, João Gonçalves, Pedro Martins, Penousal Machado, Amílcar Cardoso
A descriptive approach for automatic generation of visual blends is presented.
no code implementations • 26 Jun 2017 • Filipe Assunção, Nuno Lourenço, Penousal Machado, Bernardete Ribeiro
On the other, there is no way to evolve networks with more than one output neuron.