134 citations · 548 across the 33 of their papers we have counts for
33 papers
Neuron-centric Hebbian Learning
Andrea Ferigo, Elia Cunegatti, Giovanni Iacca
One of the most striking capabilities behind the learning mechanisms of the brain is the adaptation, through structural and functional plasticity, of its synapses. While synapses h…
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and…
Social Interpretable Reinforcement Learning
Leonardo Lucio Custode, Giovanni Iacca
Reinforcement Learning (RL) bears the promise of being a game-changer in many applications. However, since most of the literature in the field is currently focused on opaque models…
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations
Elia Cunegatti, Matteo Farina, Doina Bucur +1
Pruning-at-Initialization (PaI) algorithms provide Sparse Neural Networks (SNNs) which are computationally more efficient than their dense counterparts, and try to avoid performanc…
Self-building Neural Networks
Andrea Ferigo, Giovanni Iacca
During the first part of life, the brain develops while it learns through a process called synaptogenesis. The neurons, growing and interacting with each other, create synapses. Ho…
Quality Diversity Evolutionary Learning of Decision Trees
Andrea Ferigo, Leonardo Lucio Custode, Giovanni Iacca
Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant…
The emergence of division of labor through decentralized social sanctioning
Anil Yaman, Joel Z. Leibo, Giovanni Iacca +1
Human ecological success relies on our characteristic ability to flexibly self-organize into cooperative social groups, the most successful of which employ substantial specializati…
Reinforcement learning based adaptive metaheuristics
Michele Tessari, Giovanni Iacca
Parameter adaptation, that is the capability to automatically adjust an algorithm's hyperparameters depending on the problem being faced, is one of the main trends in evolutionary…
Interpretable AI for policy-making in pandemics
Leonardo Lucio Custode, Giovanni Iacca
Since the first wave of the COVID-19 pandemic, governments have applied restrictions in order to slow down its spreading. However, creating such policies is hard, especially becaus…
Online Distributed Evolutionary Optimization of Time Division Multiple Access Protocols
Anil Yaman, Tim van der Lee, Giovanni Iacca
With the advent of cheap, miniaturized electronics, ubiquitous networking has reached an unprecedented level of complexity, scale and heterogeneity, becoming the core of several mo…
Large-scale multi-objective influence maximisation with network downscaling
Elia Cunegatti, Giovanni Iacca, Doina Bucur
Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several metho…
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs
Leonardo Lucio Custode, Giovanni Iacca
The importance of explainability in AI has become a pressing concern, for which several explainable AI (XAI) approaches have been recently proposed. However, most of the available…
Black-box adversarial attacks using Evolution Strategies
Hao Qiu, Leonardo Lucio Custode, Giovanni Iacca
In the last decade, deep neural networks have proven to be very powerful in computer vision tasks, starting a revolution in the computer vision and machine learning fields. However…
Graph-Aware Evolutionary Algorithms for Influence Maximization
Kateryna Konotopska, Giovanni Iacca
Social networks represent nowadays in many contexts the main source of information transmission and the way opinions and actions are influenced. For instance, generic advertisement…
Seeking Quality Diversity in Evolutionary Co-design of Morphology and Control of Soft Tensegrity Modular Robots
Enrico Zardini, Davide Zappetti, Davide Zambrano +2
Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulat…
A Framework for Knowledge Integrated Evolutionary Algorithms
Ahmed Hallawa, Anil Yaman, Giovanni Iacca +1
One of the main reasons for the success of Evolutionary Algorithms (EAs) is their general-purposeness, i.e., the fact that they can be applied straightforwardly to a broad range of…
A Signal-Centric Perspective on the Evolution of Symbolic Communication
Quintino Francesco Lotito, Leonardo Lucio Custode, Giovanni Iacca
The evolution of symbolic communication is a longstanding open research question in biology. While some theories suggest that it originated from sub-symbolic communication (i.e., i…
Genetic Improvement of Routing Protocols for Delay Tolerant Networks
Michela Lorandi, Leonardo Lucio Custode, Giovanni Iacca
Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g…
Evolutionary learning of interpretable decision trees
Leonardo Lucio Custode, Giovanni Iacca
Reinforcement learning techniques achieved human-level performance in several tasks in the last decade. However, in recent years, the need for interpretability emerged: we want to…
EVO-RL: Evolutionary-Driven Reinforcement Learning
Ahmed Hallawa, Thorsten Born, Anke Schmeink +6
In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (evo-RL…
Distributed Embodied Evolution over Networks
Anil Yaman, Giovanni Iacca
In several network problems the optimum behavior of the agents (i.e., the nodes of the network) is not known before deployment. Furthermore, the agents might be required to adapt,…
Novelty Producing Synaptic Plasticity
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +2
A learning process with the plasticity property often requires reinforcement signals to guide the process. However, in some tasks (e.g. maze-navigation), it is very difficult (or i…
Regularized Evolutionary Algorithm for Dynamic Neural Topology Search
Cristiano Saltori, Subhankar Roy, Nicu Sebe +1
Designing neural networks for object recognition requires considerable architecture engineering. As a remedy, neuro-evolutionary network architecture search, which automatically se…
Learning with Delayed Synaptic Plasticity
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +2
The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration. Inspired by biology, plasticity c…