6 citations · 16 across the 8 of their papers we have counts for
6 papers · 1 filter
Towards Physical Plausibility in Neuroevolution Systems
Gabriel Cortês, Nuno Lourenço, Penousal Machado
The increasing usage of Artificial Intelligence (AI) models, especially Deep Neural Networks (DNNs), is increasing the power consumption during training and inference, posing envir…
All You Need Is Sex for Diversity
José Maria Simões, Nuno Lourenço, Penousal Machado
Maintaining genetic diversity as a means to avoid premature convergence is critical in Genetic Programming. Several approaches have been proposed to achieve this, with some focusin…
Context Matters: Adaptive Mutation for Grammars
Pedro Carvalho, Jessica Mégane, Nuno Lourenço +1
This work proposes Adaptive Facilitated Mutation, a self-adaptive mutation method for Structured Grammatical Evolution (SGE), biologically inspired by the theory of facilitated var…
Structured mutation inspired by evolutionary theory enriches population performance and diversity
Stefano Tiso, Pedro Carvalho, Nuno Lourenço +1
Grammar-Guided Genetic Programming (GGGP) employs a variety of insights from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionar…
Probabilistic Structured Grammatical Evolution
Jessica Mégane, Nuno Lourenço, Penousal Machado
The grammars used in grammar-based Genetic Programming (GP) methods have a significant impact on the quality of the solutions generated since they define the search space by restri…
Co-evolutionary Probabilistic Structured Grammatical Evolution
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). In Co-PSGE each individua…