scispace - formally typeset
G

Gianni A. Di Caro

Researcher at Carnegie Mellon University

Publications -  119
Citations -  14282

Gianni A. Di Caro is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Robot & Mobile robot. The author has an hindex of 36, co-authored 117 publications receiving 13345 citations. Previous affiliations of Gianni A. Di Caro include Dalle Molle Institute for Artificial Intelligence Research & SUPSI.

Papers
More filters
Journal ArticleDOI

Ant algorithms for discrete optimization

Marco Dorigo, +2 more
- 01 Apr 1999 - 
TL;DR: An overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and the ant colony optimization (ACO) metaheuristic is presented.
Book

The ant colony optimization meta-heuristic

Marco Dorigo, +1 more
TL;DR: This chapter contains sections titled: Combinatorial Optimization, The ACO Metaheuristic, How Do I Apply ACO?
Journal ArticleDOI

AntNet: distributed stigmergetic control for communications networks

Gianni A. Di Caro, +1 more
- 01 Aug 1998 - 
TL;DR: AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems, and showed superior performance under all the experimental conditions with respect to its competitors.
Book

Applications of Evolutionary Computing

Mario Giacobini, +8 more
TL;DR: EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming, Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing, and more.
Journal ArticleDOI

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Alessandro Giusti, +11 more
TL;DR: This work proposes a different approach to perceive forest trials based on a deep neural network used as a supervised image classifier that outperforms alternatives, and yields an accuracy comparable to the accuracy of humans that are tested on the same image classification task.