activity
20172025
most citedDENSER: Deep Evolutionary Network Structured Representation

105 citations · 282 across the 25 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.NE2023★ 1 cited

SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks

Henrique Branquinho, Nuno Lourenço, Ernesto Costa

Spiking Neural Networks (SNNs) have attracted recent interest due to their energy efficiency and biological plausibility. However, the performance of SNNs still lags behind traditi…

cs.NE2023

Reducing the Price of Stable Cable Stayed Bridges with CMA-ES

Gabriel Fernandes, Nuno Lourenço, João Correia

The design of cable-stayed bridges requires the determination of several design variables' values. Civil engineers usually perform this task by hand as an iteration of steps that s…

cs.NE2023

Automatic Design of Telecom Networks with Genetic Algorithms

João Correia, Gustavo Gama, João Tiago Guerrinha +4

With the increasing demand for high-quality internet services, deploying GPON/Fiber-to-the-Home networks is one of the biggest challenges that internet providers have to deal with…

cs.NE2023

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…

cs.NE2023★ 6 cited

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…

cs.NE2023

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…