Search Results for author: Giovanni Iacca

Found 34 papers, 15 papers with code

Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems

no code implementations12 Jun 2024 Ryan Zhou, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Martin Fyvie, Giovanni Iacca, John McCall, Niki van Stein, David Walker, Ting Hu

Additionally, we discuss the application of XAI principles within EC itself, examining how these principles can shed some light on the behavior and outcomes of EC algorithms in general, on the (automatic) configuration of these algorithms, and on the underlying problem landscapes that these algorithms optimize.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Frustratingly Easy Test-Time Adaptation of Vision-Language Models

1 code implementation28 May 2024 Matteo Farina, Gianni Franchi, Giovanni Iacca, Massimiliano Mancini, Elisa Ricci

Thanks to its simplicity and comparatively negligible computation, ZERO can serve as a strong baseline for future work in this field.

Test-time Adaptation

Influence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms

1 code implementation16 May 2024 Stefano Genetti, Eros Ribaga, Elia Cunegatti, Quintino Francesco Lotito, Giovanni Iacca

Among the various methods for solving the IM problem, evolutionary algorithms (EAs) have been shown to be particularly effective.

Evolutionary Algorithms

MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning

1 code implementation CVPR 2024 Matteo Farina, Massimiliano Mancini, Elia Cunegatti, Gaowen Liu, Giovanni Iacca, Elisa Ricci

In this challenging setting, the transferable representations already encoded in the pretrained model are a key aspect to preserve.

Transfer Learning

Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time

1 code implementation27 Mar 2024 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.

Fairness

Neuron-centric Hebbian Learning

1 code implementation16 Feb 2024 Andrea Ferigo, Elia Cunegatti, Giovanni Iacca

To overcome this limitation, we propose a novel plasticity model, called Neuron-centric Hebbian Learning (NcHL), where optimization focuses on neuron- rather than synaptic-specific Hebbian parameters.

Social Interpretable Reinforcement Learning

no code implementations27 Jan 2024 Leonardo Lucio Custode, Giovanni Iacca

Our method mimics a social learning process, where each agent in a group learns to solve a given task based both on its own individual experience as well as the experience acquired together with its peers.

reinforcement-learning Reinforcement Learning (RL)

Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations

1 code implementation26 May 2023 Elia Cunegatti, Matteo Farina, Doina Bucur, Giovanni Iacca

With these novelties, we show the following: (a) The proposed MGE allows to extract topological metrics that are much better predictors of the accuracy drop than metrics computed from current input-agnostic BGEs; (b) Which metrics are important at different sparsity levels and for different architectures; (c) A mixture of our topological metrics can rank PaI algorithms more effectively than Ramanujan-based metrics.

Relation

Self-building Neural Networks

1 code implementation3 Apr 2023 Andrea Ferigo, Giovanni Iacca

We compare our proposed SBNN with traditional neural networks (NNs) over three classical control tasks from OpenAI.

Quality Diversity Evolutionary Learning of Decision Trees

no code implementations17 Aug 2022 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).

OpenAI Gym

The emergence of division of labor through decentralized social sanctioning

no code implementations10 Aug 2022 Anil Yaman, Joel Z. Leibo, Giovanni Iacca, Sang Wan Lee

Here we show that by introducing a model of social norms, which we regard as emergent patterns of decentralized social sanctioning, it becomes possible for groups of self-interested individuals to learn a productive division of labor involving all critical roles.

Reinforcement learning based adaptive metaheuristics

1 code implementation24 Jun 2022 Michele Tessari, Giovanni Iacca

We demonstrate the applicability of this framework on two algorithms, namely Covariance Matrix Adaptation Evolution Strategies (CMA-ES) and Differential Evolution (DE), for which we learn, respectively, adaptation policies for the step-size (for CMA-ES), and the scale factor and crossover rate (for DE).

reinforcement-learning Reinforcement Learning (RL)

Online Distributed Evolutionary Optimization of Time Division Multiple Access Protocols

no code implementations27 Apr 2022 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 modern applications such as smart industry, smart buildings and smart cities.

Large-scale multi-objective influence maximisation with network downscaling

1 code implementation13 Apr 2022 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.

Interpretable AI for policy-making in pandemics

no code implementations8 Apr 2022 Leonardo Lucio Custode, Giovanni Iacca

For this reason, several works have applied machine learning techniques, often with the help of special-purpose simulators, to generate policies that were more effective than the ones obtained by governments.

Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs

no code implementations10 Feb 2022 Leonardo Lucio Custode, Giovanni Iacca

However, they struggle when working with raw data, especially when the input dimensionality increases and the raw inputs alone do not give valuable insights on the decision-making process.

Decision Making Evolutionary Algorithms +3

Graph-Aware Evolutionary Algorithms for Influence Maximization

1 code implementation30 Apr 2021 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.

Evolutionary Algorithms Marketing

Black-box adversarial attacks using Evolution Strategies

no code implementations30 Apr 2021 Hao Qiu, Leonardo Lucio Custode, Giovanni Iacca

Several methods able to generate adversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios.

Image Classification

A Signal-Centric Perspective on the Evolution of Symbolic Communication

1 code implementation31 Mar 2021 Quintino Francesco Lotito, Leonardo Lucio Custode, Giovanni Iacca

The evolution of symbolic communication is a longstanding open research question in biology.

A Framework for Knowledge Integrated Evolutionary Algorithms

no code implementations31 Mar 2021 Ahmed Hallawa, Anil Yaman, Giovanni Iacca, Gerd Ascheid

Notably, the KIEA framework is EA-agnostic (i. e., it works with any evolutionary algorithm), problem-independent (i. e., it is not dedicated to a specific type of problems), expandable (i. e., its knowledge base can grow over time).

Evolutionary Algorithms

Genetic Improvement of Routing Protocols for Delay Tolerant Networks

1 code implementation12 Mar 2021 Michela Lorandi, Leonardo Lucio Custode, Giovanni Iacca

We apply this methodology, in silico, to six test cases of urban networks made of hundreds of nodes, and find that GI produces consistent gains in delivery probability in four cases.

Evolutionary learning of interpretable decision trees

1 code implementation14 Dec 2020 Leonardo Lucio Custode, Giovanni Iacca

We present a two-level optimization scheme that combines the advantages of evolutionary algorithms with the advantages of Q-learning.

Evolutionary Algorithms OpenAI Gym +2

EVO-RL: Evolutionary-Driven Reinforcement Learning

no code implementations9 Jul 2020 Ahmed Hallawa, Thorsten Born, Anke Schmeink, Guido Dartmann, Arne Peine, Lukas Martin, Giovanni Iacca, A. E. Eiben, Gerd Ascheid

Furthermore, we propose that this distinction is decided by the evolutionary process, thus allowing evo-RL to be adaptive to different environments.

OpenAI Gym reinforcement-learning +1

Distributed Embodied Evolution over Networks

no code implementations28 Mar 2020 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.

Novelty Producing Synaptic Plasticity

no code implementations10 Feb 2020 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

A learning process with the plasticity property often requires reinforcement signals to guide the process.

Regularized Evolutionary Algorithm for Dynamic Neural Topology Search

no code implementations15 May 2019 Cristiano Saltori, Subhankar Roy, Nicu Sebe, Giovanni Iacca

Although very effective, evolutionary algorithms rely heavily on having a large population of individuals (i. e., network architectures) and is therefore memory expensive.

Evolutionary Algorithms Neural Architecture Search +1

Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

no code implementations2 Apr 2019 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy

Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons.

Learning with Delayed Synaptic Plasticity

no code implementations22 Mar 2019 Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, George Fletcher, Mykola Pechenizkiy

Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i. e. rules that update synapses based on the neuron activations and reinforcement signals.

Evaluating MAP-Elites on Constrained Optimization Problems

1 code implementation2 Feb 2019 Stefano Fioravanzo, Giovanni Iacca

As such, it could be used in the future as an effective building-block for designing new constrained optimization algorithms.

Multi-Strategy Coevolving Aging Particle Optimization

no code implementations11 Oct 2018 Giovanni Iacca, Fabio Caraffini, Ferrante Neri

We propose Multi-Strategy Coevolving Aging Particles (MS-CAP), a novel population-based algorithm for black-box optimization.

Distributed optimization in wireless sensor networks: an island-model framework

no code implementations5 Oct 2018 Giovanni Iacca

We perform extensive tests of different DOWSN configurations on a benchmark made up of continuous optimization problems; we analyze the influence of the network parameters (number of nodes, inter-node communication period and probability of accepting incoming solutions) on the optimization performance.

Anomaly Detection Distributed Optimization

Compact Optimization Algorithms with Re-sampled Inheritance

no code implementations12 Sep 2018 Giovanni Iacca, Fabio Caraffini

The resulting compact algorithms with RI are tested on the CEC 2014 benchmark functions.

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