134 citations · 548 across the 33 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu +3
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning i…
Evaluating MAP-Elites on Constrained Optimization Problems
Stefano Fioravanzo, Giovanni Iacca
Constrained optimization problems are often characterized by multiple constraints that, in the practice, must be satisfied with different tolerance levels. While some constraints a…
Multi-Strategy Coevolving Aging Particle Optimization
Giovanni Iacca, Fabio Caraffini, Ferrante Neri
We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization. In a memetic fashion, MS-CAP combines two components w…
Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms
D. Bucur, G. Iacca, M. Gaudesi +2
In novel forms of the Social Internet of Things, any mobile user within communication range may help routing messages for another user in the network. The resulting message deliver…
Memetic Viability Evolution for Constrained Optimization
A. Maesani, G. Iacca, D. Floreano
The performance of evolutionary algorithms can be heavily undermined when constraints limit the feasible areas of the search space. For instance, while Covariance Matrix Adaptation…
Distributed optimization in wireless sensor networks: an island-model framework
Giovanni Iacca
Wireless Sensor Networks (WSNs) is an emerging technology in several application domains, ranging from urban surveillance to environmental and structural monitoring. Computational…
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Anil Yaman, Decebal Constantin Mocanu, Giovanni Iacca +2
Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architect…